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IEEE COMSOC MMTC E-Letter March
Research Paper / Mar 2013

 

 

IEEE COMSOC MMTC E-Letter 

 

http://www.comsoc.org/~mmc/ 1/44    Vol.8, No.2, March 2013 

 

 

 

 

 

MULTIMEDIA COMMUNICATIONS TECHNICAL COMMITTEE  

 

IEEE COMMUNICATIONS SOCIETY 

http://www.comsoc.org/~mmc 

 

 

 

E-LETTER  

 

 

Vol. 8, No. 2, March 2013 

 

 

 

CONTENTS 

 

Message from MMTC Chair ........................................................................................................ 3 

EMERGING TOPICS: SPECIAL ISSUE ON ........................................................................... 4 

QoE Aware Optimization in Mobile Networks ........................................................................... 4 

 

Guest Editors: Tasos Dagiuklas, TEI of Mesolonghi, Greece, Weisi Lin, Nanyang 

Technological University, Singapore, Adlen Ksentini, University of Rennes 1, France ......... 4 

 

A QoE cross layer approach to model media experiences ......................................................... 6 

Andrew Perkis ........................................................................................................................... 6 

NTNU, Norway .......................................................................................................................... 6 

andrew@iet.ntnu.no .................................................................................................................. 6 

 

Context Aware Quality of Experience for Audio-Visual Service Groups ................................ 9 

M. Tourad Diallo

 

1

, H. Moustafa

 

2

, H.Afifi

 

3

, N. Marechal

 

1 ........................................................ 9 

(1) Orange Labs, France, (2) Intel Labs USA, (3) Institut Mines-Télécom France ................. 9 

{mamadoutourad.diallo, nicolas.marechal}@orange.com, 

hassnaa.moustafa@intel.com, hossam.afifi@mines-telecom.fr .............................................. 9 

 

No-reference IPTV Video Quality Assessment Based on End-to-End Visual 

 

Distortion Estimation .................................................................................................................. 12 

Ning Liao and Zhibo Chen ...................................................................................................... 12 

Technicolor Research & Innovation, Beijing, China ................................................................ 12 

ning.liao@technicolor.com, chenzb@ieee.org ....................................................................... 12 

 

Video Quality as a Driver for Traffic Management with Multiple Subscriber Classes ........ 15 

Martín Varela and Janne Seppänen ........................................................................................ 15 

VTT Technical Research Centre of Finland ............................................................................ 15 

{martin.varela, janne.seppanen}@vtt.fi ................................................................................. 15 

 

Cross-layer Design for Quality-Driven Multi-user Multimedia .............................................. 18 

Transmission in Mobile Networks ............................................................................................. 18 

 

Maria G. Martini ..................................................................................................................... 18 

Kingston University London .................................................................................................... 18 

{m.martini}@kingston.ac.uk ................................................................................................... 18 

 

INDUSTRIAL COLUMN: SPEICAL ISSUE ON .................................................................... 21 

“DYNAMIC ADAPTIVE STREAMING OVER HTTP” ....................................................... 21 

 

Guest Editors: Alex Giladi, Zhenyu Wu, Futurewei Technologies, U.S.A .............................. 21 

and Guosen Yue, NEC Laboratories America......................................................................... 21 

 

MPEG DASH: A Brief Introduction ......................................................................................... 23 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

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Alex Giladi FutureWei Technologies  400 Crossing Blvd., Bridgewater NJ, 08807 

 

Email: alex.giladi@huawei.com ........................................................................................... 23 

Optimizing DASH Delivery Services over Wireless Networks ................................................ 27 

 

Ozgur Oyman, Intel Labs, Santa Clara, CA 95054 USA ........................................................ 27 

ozgur.oyman@intel.com .......................................................................................................... 27 

 

Fair Share Dynamic Adaptive Streaming over HTTP ............................................................. 30 

Christopher Müeller, Stefan Lederer, and Christian Timmerer ............................................. 30 

Multimedia Communication (MMC) Research Group, Institute of Information 

 

Technology (ITEC) ................................................................................................................ 30 

Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria ...................................................... 30 

{Christopher.Mueller, Stefan.Lederer, Christian.Timmerer}@itec.aau.at ............................. 30 

 

Quality Driven Streaming Using MPEG-DASH ....................................................................... 34 

Shaobo Zhang, Yangpo Xu, Peiyun Di, Alex Giladi, Changquan Ai, Xin Wang ..................... 34 

Huawei Technologies .............................................................................................................. 34 

 

User-Adaptive Mobile Video Streaming Using MPEG-DASH ............................................... 39 

Yuriy A. Reznik, InterDigital Communications, Inc. 9710 Scranton Road, San Diego, 

 

CA 92122 Email: yuriy.reznik@interdigital.com .................................................................. 39 

Call For Papers ............................................................................................................................ 42 

MMTC OFFICERS ……………………………………………………………………............44 

 

              

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

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Message from MMTC Chair 

 

Dear MMTC colleagues: 

 

Wish you all a pleasant holiday season and a fruitful new year in 2013. It is really a great honor for me to serve as the 

Asia vice-chair for this vital ComSoc Committee during the period 2012-2014! As part of my duties, I have 

contributed to the initial setting of the Interest Groups (IGs) and I am starting to work on the promotion of special 

issues with top journals and the webseminars. 

 

Concerning our IGs, I really believe that these represent the core of our networking and scientific activities and I 

warmly invite all of you to select one or more IG(s) to get involved by contacting the chair(s) so as to take part as key 

member. The activities of the IGs include, among others, the organization of workshops, sessions and conferences 

with the involvement of the MMTC, the editing of special issues in major IEEE journals, the setting of invited talks 

trough conference calls that can be of interest for our community and the rest of the ComSoc members. While these 

are the major activities, some others can be carried out following the specific IG topics, such as the contribution to 

standardization activities. 

 

The two iniatives will not success without strong support from our IG leaders and contributing members. For the 

special-issue effort, I will be working with Dr. Chonggang Wang to first identify a list of potential transactions, 

journals and magazines that are relavant to our TC. This list will be shared with our IG leaders who will in turn 

propose potential topics for a chosen venue We will first socialize the topics with EiC(s) and AEs and work with the 

IG(s) to develop the full proposal. It is our hope to achieve the largest efficiency via our collaborative efforts, ideally 

organizing up to 7-10 speciall issues. We also encourage multiple IGs to collaboratively propose topics that are 

relavent. For the webseminars, we would like to call for recommendations from our IG(s) and all the members with 

MMTC. Upon receiving your recommendations and/or volunteerings, we will work with IEEE ComSoc to set up the 

infrastructure and announcement the talks to potential audience. During the two-year term, we will aim to have a 

quarterly webseminar for our MMTC. 

 

I would like to thank all the IG chairs and co-chairs for the work they have already done and will be doing for the 

success of MMTC and hope that any of you will find the proper IG of interest to get involved in our community! 

 

 

 

 

Yonggang Wen 

Asia Vice-Chair of Multimedia Communications TC of IEEE ComSoc 

 

 

 

 

 

 

 

 

 

 

 

 

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EMERGING TOPICS: SPECIAL ISSUE ON  

 

 

 

QoE Aware Optimization in Mobile Networks 

 

Guest Editors: Tasos Dagiuklas, TEI of Mesolonghi, Greece, Weisi Lin, Nanyang Technological 

 

University, Singapore, Adlen Ksentini, University of Rennes 1, France

 

Quality of Experience (QoE) is the overall performance 

of an end-to-end networking system from users’ 

perspective. It is basically a subjective measure of end-

to-end performance at the service level, from the point 

of view of users. As such, it is also an indicator of how 

well the network satisfies users’ preferences. The QoE 

reflects the perceptive output of the network and its 

performance with respect to the expected quality by 

end users and it is the result of the perceived effects of 

all the Quality of Service (QoS) mechanisms across 

network and application layers.  QoE optimization of 

multimedia applications across mobile networks faces 

the following challenges: physical impairments, 

bandwidth variability, session mobility and 

maintenance while the users hands-off across inter-

technology networks. 

This special issue of E-Letter focuses on the recent 

progresses of QoE Aware Optimization in Mobile 

Networks. It is the great honor of the editorial team to 

have five leading research groups, from both academia 

and industry laboratories, to report their solutions for 

meeting these challenges and share their latest research 

results. 

 

In the first article entitled, “A QoE cross layer 

approach to model media experiences”, Andrew Perkis, 

from Norwegian University of Science and Technology 

presents QoE optimization in Immersive Media 

Technology Experiences (IMTE) through cross layer 

dynamics. The IMTE represents a holistic view of a 

user’s experience within the media sphere following 

content from acquisition, representation, interaction to 

delivery and usage and finally the business model. To 

optimize QoE, the following aspects for each of the 

five stages and their interconnections have been 

defined:  the User, Content and Infrastructure. 

  

Dialo from Orange Labs, Hassnaa Moustafa from Intel 

Labs, Affifi from Institut Mines Télécom and Marechal 

from Orange Labs author the second article, “Context 

Aware Quality of Experience for Audio-Visual Service 

 

Groups”. The authors propose a new notion of user 

experience based on context-awareness with extended 

context information (network context, device context, 

user context, content context,…), aiming to improve 

the user satisfaction using the full media distribution 

chain (domestic, access and core networks along with 

 

content delivery). The authors argue that global QoE 

should be used for groups sharing a resource to 

optimize the overall distribution parameters. The paper 

analyzes the complete distribution chain of multimedia 

content (device, access network, content network) to 

identify which parameters should be first adjusted to 

have an immediate influence on the QoE improvement. 

 

The third article is contributed by Ning Liao and Zhibo 

Chen from Technicolor Research & Innovation, and 

the title is “No-reference IPTV Video Quality 

Assessment Based on End-to-End Visual Distortion 

 

Estimation”. The authors present a model for parsing-

mode P.NBAMS in IPTV and Mobile video streaming 

scenarios, which demonstrated the best performance in 

ITU-T P.NBAMS standard competition. In this model, 

the visual distortions of three types of above-mentioned 

artifacts are modeled separately, and then the mutual 

influence of perceptible compression artifacts and 

slicing/freezing artifacts is modeled by linearly 

combining the output of the two worst quality levels of 

the individual artifact models. 

 

Martín Varela and Janne Seppänen presented a QoE-

based traffic control system by VTT in the fourth 

article, “Video Quality as a Driver for Traffic 

Management with Multiple Subscriber Classes”. The 

authors propose a composite approach to manage the 

traffic in order to provide adequate QoE to the users. 

This is accomplished through a subscriber-based 

differentiation scheme (implemented, without loss of 

generality, with two classes of users, namely premium 

and normal ones), and a traffic management scheme 

based on both access control and application-based 

traffic differentiation.  

 

The last article is entitled “Cross-layer design for 

quality-driven multi-user multimedia  transmission in 

 

mobile networks”, from Maria Martini at Kingston 

University. In this paper, the author presents the main 

aspects of QoE-driven cross-layer for multimedia 

transmission over mobile networks, highlighting design 

issues and open points.  

 

While this special issue is far from a complete 

coverage on this exciting research area, we hope that 

the five invited articles give the audiences a taste of the 

 

 

 

 

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main recent activities in this area, and provide them an 

opportunity to discuss, explore and collaborate in the 

related fields. Finally, we would like to thank all the 

authors for their great contribution and the E-Letter 

Board for making this special issue possible. 

 

 

Tasos Dagiuklas received the 

received the Engineering Degree 

from the University of Patras-

Greece in 1989, the M.Sc. from the 

University of Manchester-UK in 

 

1991 and the Ph.D. from the University of Essex-UK in 

1995, all in Electrical Engineering. He is Assistant 

Professor at the Department of Telecommunication 

Systems and Networks, TEI of Mesolonghi, Greece. 

He is the Leader of the CONES research group 

(http://www.tesyd.teimes.gr/cones). Dr Dagiuklas is a 

Vice-Chair for IEEE MMTC QoE WG and Key 

Member of IEEE MMTC MSIG and 3DRPC WGs. He 

is also an active member of IEEE P1907.1 

Standardiazation WG. He is a reviewer for journals 

such as IEEE Transactions on Multimedia, IEEE 

Communication Letters and IEEE Journal on Selected 

Areas in Communications. His research interests 

include 2D/3D video transmission/adaptation/rate 

control across heterogeneous wireless networks, P2P 

video streaming and service provisioning across Future 

Internet architectures. He is a Senior Member of IEEE 

and Technical Chamber of Greece. 

 

Weisi Lin received his Ph.D from 

King’s College, London University, 

U.K. He was the Lab Head, Visual 

Processing, and the Acting 

Department Manager, Media 

Processing, Institute for Infocomm 

Research, Singapore. Currently, he is 

an Associate Professor in the School 

 

of Computer Engineering, Nanyang Technological 

University, Singapore. His research interests include 

image processing, perceptual quality evaluation, video 

compression, multimedia communication and computer 

vision. He has published 200+ refereed papers in 

international journals and conferences. He is on the 

editorial boards of IEEE Trans. on Multimedia, IEEE 

SIGNAL PROCESSING LETTERS and Journal of 

Visual Communication and Image Representation. He 

has been elected as a Distinguished Lecturer of 

APSIPA (2012/3). He is the Lead Technical Program 

Chair for PCM2012, and a Technical Program Chair 

for IEEE ICME2013. He is a fellow of Institution of 

Engineering Technology, and an Honorary Fellow, 

Singapore Institute of Engineering Technologists. 

 

 Adlen Ksentini Adlen KSENTINI 

is an Associate Professor at the 

University of Rennes 1, France. He 

is a member of the INRIA Rennes 

team Dionysos. He received an 

M.Sc. in telecommunication and 

 

multimedia networking from the University of 

Versailles . He obtained his Ph.D. degree in computer 

science from the University of Cergy-Pontoise in 2005, 

with a dissertation on QoS provisioning in IEEE 

802.11-based networks. His other interests include: 

future Internet networks, cellular networks, green 

networks, QoS, QoE and multimedia transmission. Dr. 

Ksentini is involved in several national and european 

projects on QoS and QoE support in Future Internet 

Networks. Dr. Ksentini is a co-author of over 50 

technical journal and international conference papers. 

Dr. Ksentini has been in the technical program 

commitee of major IEEE ComSoc conferences, 

ICC/Globecom, ICME, WCNC, PIMRC. Dr. Ksentini 

is Vice-Chair for IEEE MMTC Interest Group on QoE.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

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A QoE cross layer approach to model media experiences 

 

Andrew Perkis 

 

NTNU, Norway 

 

andrew@iet.ntnu.no

 

1. Introduction 

A Media experience supports natural interactions 

between people and their environment. The media 

considered consists of audio and visual presentations 

and their interactions as well as user interactions 

including traditional interactivity as well as novel 

methods through Natural User Interfaces creating real 

world presence.  

In order to find a measure for the user’s perceived 

quality of the experience we need to shift from using 

simple Quality of Service (QoS) as a measure of the 

quality to the broader concept of Quality of Experience 

[8]. The current assumptions of QoE in the media 

representation and delivery community, with close 

links to other fields such as Psychology and social 

sciences, are shown in Figure 1. 

 

 

 

 

 

Figure 1 QoE influencing factors 

 

 

A definition based on these assumptions is given in the 

Qualinet [4] White paper published in 2012 [1]. 

One of the earliest works on QoE can be found in [5] 

which define QoE as a measure of the impact of 

content on a specific user, in a specific context. This 

can be measured either through a subjective assessment, 

or estimated through a model based on the content, 

specific user and specific context parameters. Another 

early approach of modeling QoE is found in [2]. It 

follows a traditional methodology where the user’s 

perception is measured by formal subjective 

evaluations. The results of these evaluations are 

considered as ground truth and used as a basis for 

 

developing highly correlated objective metrics for 

quality. 

QoE optimisations in multimedia communications has 

lead into developing new digital media enabling more 

immersive experiences. The media considered still 

consists of audio and visual presentations, but now 

enriched by new functionality enabling interactivity. 

The ultimate goals are to digitally create real world 

presence and describe and define the work within a 

new field denoted Immersive Media Technology 

Experiences (IMTE). 

 

 

 

Figure 2 Cross disciplinary spiral research approach 

 

QoE 

 

 

IMTE is highly cross disciplinary incorporating several 

disciplines including Media Technology, Information 

and Communication Technology, and Media Studies. 

In this way IMTE can encompass diverse core 

competencies covering fields such as communications, 

information retrieval, entertainment and social 

networks. Our approach is to use a spiral based 

research approach, as illustrated in Figure 2, where 

IMTE is the propeller advancing the field and 

maintaining the spin of new ideas and approaches. 

In this paper, we present a new media experience 

model based on the spiral research approach and show 

how QoE is used as in the cross layer optimization 

between each layer in order to optimize the user’s 

experience. 

 

 

2. QoE optimization 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

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To achieve QoE optimization through cross layer 

dynamics a holistic multidisciplinary cross layer effort 

is required. IMTE presents a holistic view of a user’s 

experience within the media sphere following content 

from capture, representation, interaction to delivery 

and usage and finally into the business model as shown 

in Figure 3, where QoE is the driving force for 

quantifying the users experience at each stage. 

To optimize QoE we need to identify the research 

domains for each of the five stages and their 

interconnections. Figure 4 shows three identified areas 

and their links; the User, Content and Infrastructure. 

 

 

Figure 3 A Holistic view of the media experience 

 

 

The glue in the linking is by the Users interacting with 

the Content through devices connected to the 

Infrastructure. Such Interactions and Devices demands 

New Digital Media and abilities for the Content to 

Adapt to User requests and the available Infrastructure 

(Networked Media Handling). This structure allows for 

piloting new advanced applications and services such 

as Digital Storytelling, Digital Art, Serious gaming, 

Presence and immersive experience (interactivity) etc. 

Some example devices are [3]: 

 

 

• Mobile phones 

 

• Pads (iPAD and Androids) 

 

• Interactive tables 

 

• Screens and larger displays 

 

• Cinema 

 

 

2. The media experience model 

 

For the users media started with storytelling and wall 

drawing around the fire in the caves of early men. 

Media technology systems are evolved versions of this 

good old storytelling and wall drawing, which 

hopefully offer the same or more immersive and rich 

experience. Today multimedia is about sharing 

 

experiences (real or imaginary) with others. These 

experiences can be modeled as shown in the proposed 

media experience model in Figure 5. 

 

 

 

Figure 4 Research areas for IMTE 

 

 

QoE is the overriding factor in the model and is seen as 

a tool for monitoring and managing the users 

experience at each of the interfaces between the model 

layers, providing cross layer optimization. 

 

 

 

 

 

Figure 5. The media experience model. 

 

 

The media experience model layers 

The first layers in the model consider the physical 

representation and delivery of the content. Today’s 

media content is evolving around optimal utilization of 

2D media and has focused on HD (High Definition) 

issues of resolution, frame rates, dynamic range, color 

space and formats. There are numerous advances in 

these fields, amongst others Ultra High Definition TV 

 

 

 

 

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(UHDTV), High Dynamic Range (HDR) and 3D. The 

future looks at increasing the user’s experience by 

moving to multi-scopic, multi-view, free viewpoint and 

omnidirectional. Together with the advances in audio 

technology all the way to auralization and 3D audio we 

see the possibility of offering Interactive holistic 

rendering of our real world to the User, with the 

ultimate goal being to digitally create real world 

presence where we can build business models and an 

economy based on the Eco system at the top layer. As 

an example of a concrete cross layer optimisation we 

see the interaction between the Content and Delivery 

layer by efforts within Networked Media Handling. 

 

 

Media technology and art 

In order to work on quality modeling and 

measurements between the layers in the model we have 

to work in an experimental setting and design our own 

novel content. The experimental dimension leads to our 

work bridging the technology and art where the content 

itself becomes an exhibition. An example of this is 

Chroma Space, where the experimental results are 

published as a scientific paper [6], while the content is 

exhibited as a piece of art [7]. 

 

 

4. Conclusion 

In this paper we have proposed a new media 

experience model and motivated for an experimental 

approach for QoE modeling and assessment in order to 

optimize the users QoE. 

 

 

References 

[1] P. L. Callet, S. Möller and A. Perkis, “Qualinet White 

 

Paper on Definitions of Quality of Experience,” 

European Network on Quality of Experience in 

Multimedia Systems and Services (COST Action IC 

1003), Lausanne, Switzerland, Version 1.1, June 3, 2012. 

 

[2] Perkis, S. Munkeby and O. I. Hillestad, “A Model for 

Measuring Quality of Experience,” 7th Nordic Signal 

Processing Symposium (NORSIG 2006), pp. 198-201, 

june 2006.  

 

[3] Perkis, Y. Abdeljaoued, C. Christopoulos, T. Ebrahimi 

and J. Chicaro, “Universal multimedia access from 

wired and wireless systems,” vol. 20, no. 3, pp. 387-402, 

2001.  

 

[4] Qualinet, “COST action IC1003 – European network on 

Quality of Experience in multimedia systems and 

services,” [Online]. Available: http://www.qualinet.eu/. 

 

[5] T. Ebrahimi, “Quality of Experience - A new look into 

quality and its impact in future personal 

communications, Keynote at Workshop on Quality of 

 

Service in Mobile Multimedia Networks,” YRP, Japan, 

February 2001., [Online]. Available: 

http://www.slideshare.net/touradj_ebrahimi. [Accessed 7 

November 2012]. 

 

[6] Mansilla, Wendy Ann; Puig, Jordi; Perkis, Andrew; 

Ebrahimi, Touradj. Chroma space: affective colors in 

interactive 3d world. In Proceedings of the 18th ACM 

International Conference on Multimedia 2010 (MM 

2010). ACM New York, NY, USA, Oct, 2010. Doi: 

10.1145/1873951.1874239 

 

[7] Mansilla, Wendy Ann; Puig, Jordi; Perkis, Andrew; 

Ebrahimi, Touradj. Chroma Space. [Exhibition 

catalogue]. Colorito: An Interactive Renaissance of 

Colours. ACM & Titivillus Mostre Editoria; Pisa, Italy, 

Oct, 2010.  

 

[8] Perkis, "Quality of Experience," SPIE Newsroom, 23 

January 2013. [Online]. Available: 

http://spie.org/x1004.xml. 

 

 

 

 

Andrew Perkis received 

his Siv.Ing and Dr. 

Techn. Degrees in 1985 

and 1994, respectively. 

In 2008 he received an 

executive Master of 

Technology Management 

in cooperation from 

NTNU, NHH and NUS 

(Singapore). Since 1993 

he has held the position 

 

of Associate Professor at the Department of 

Telecommunications at NTNU and as full professor 

since 2003. Currently he is focusing on Multimedia 

Signal Processing, specifically within methods and 

functionality of content representation, quality 

assessment and its use within the media value chain in 

a variety of applications. He was one of the founding 

authors of concepts such as Universal Multimedia 

Access and Quality of Experience (QoE). He is also 

involved in setting up directions and visions for new 

research within media technology and entertainment as 

well as directions for innovations in Immersive Media 

Technology Experiences. He is member of The 

Norwegian Academy of Technological Sciences 

(NTVA), senior member of the IEEE, member of ACM 

and member of The Norwegian Society of Chartered 

Engineers (TEKNA). He is Vice Chair of the COST 

Action IC1003 (QUALINET) which gathers more than 

150 researchers in a consortium related to QoE issues 

in multimedia systems and services. 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

http://www.comsoc.org/~mmc/ 9/44 Vol.8, No.2, March 2013 

 

 

Context Aware Quality of Experience for Audio-Visual Service Groups 

 

M. Tourad Diallo

1

, H. Moustafa

 

2

, H.Afifi

 

3

, N. Marechal

 

1

 

 

(1) Orange Labs, France, (2) Intel Labs USA, (3) Institut Mines-Télécom France 

 

{mamadoutourad.diallo, nicolas.marechal}@orange.com, hassnaa.moustafa@intel.com, 

hossam.afifi@mines-telecom.fr

 

1. Introduction 

 

With the network heterogeneity and increasing demand 

for multimedia audio-visual services and applications, 

Quality of Experience (QoE) has become a crucial 

determinant of the success or failure of these 

applications and services. As there is burgeoning need 

to understand human hedonic and aesthetic quality 

requirements, QoE appears as a measure of the users’ 

satisfaction from a service through providing an 

assessment of human expectations, feelings, 

perceptions, cognition and acceptance with respect to a 

particular  service or application [1]. This helps 

network operators and service providers to know how 

users perceive the quality of video, audio, and image 

which is a prime criterion for the quality of multimedia 

and audio-visual applications and services. QoE is a 

multidimensional concept consisting of both objective 

(e.g., human physiological and cognitive factors) and 

subjective (e.g., human psychological factors) aspects.  

There are several methods to measure and predict QoE 

for multimedia in fixed and mobile networks [10,11]. 

There are also commercial tools as conviva, skytide 

and mediamelon. [4, 5 and 6].  

Measuring client satisfaction is not something new 

within service providers business. What is new is the 

ability to deduce it automatically, dynamically and 

contextually. We define in this paper a new notion for 

contextual group QoE with more parameters in the user 

environment to accurately predict the QoE. To 

maximize the end-user satisfaction, and given those 

dynamic score evaluations it will be necessary to do 

some adaptation at both the application layer (e.g. 

choice of the compression parameters, change bitrate, 

choice of layers which will be send to client), the 

network layer (e.g. delivery means, unicast, multicast, 

choice of access network) and the delivery side (source 

server choice, CDN delivery from a cloud...) [7]. 

 

 

2.  Context-Aware QoE 

 

To improve the QoE and maximizing the user’s 

satisfaction for a service, we extend the QoE notion 

through coupling it with different context concerning 

the user (preferences, content consumption style, level 

of interest, location, …..) and his environment 

including the used terminal (capacity, screen size, ..) 

and network (available bandwidth, delay, jitter, packet 

loss rate..).  

Human moods, expectations, feelings and behavior 

could change with variation in his/her context [8]. The 

 

context-aware QoE notion presents an exact 

assessment of QoE with respect to contextual 

information of a user in a communication ecosystem. 

To measure user experience, context information needs 

to be gathered as a first step in a dynamic and real-time 

manner during services access [3]. This information 

includes: i): devices context (capacity, screen size, 

availability of network connectivities …), ii) network 

context (jitter, packet loss, available bandwidth …), iii) 

user context (Who is the user, his preferences, his 

consumption style, gender, age…), and iv) user 

localization.  User localization considers both the 

physical location of the user (indoor or outdoor) and 

the user location within the network that we call the 

Geographical Position Within the Network (GPWN). 

For the physical localization, user’s can be localized 

indoors (within their domestic sphere for examples) 

through the Wi-Fi or Bluetooth techniques [9]. For the 

outdoor localization of users, GPS (Global Positioning 

System), Radio Signal Strength Indicator (RSSI), and 

Cell-ID (based on the knowledge of base stations 

locations and coverage) are commonly used.  

After context information gathering, a second step is to 

personalize and adapt the content and the content 

delivery means according to the gathered context 

information for improving user’s satisfaction of 

services and for better resources consumption. Figure 1 

illustrates our vision of content adaptation and Figure 2 

describes the QoE evaluation process.  

 

 

Figure 1: New vision of content adaptation 

 

 

 

3. QoE Measuring Techniques 

Internet Service Providers (ISPs) use Quality of 

Service (QoS) parameters such as bandwidth, delay or 

jitter to guarantee good service quality. QoS 

parameters [10] are not the only parameters affecting 

QoE. The challenging question is how to quantify the 

QoE measure. In general there are three main 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

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techniques for measuring the QoE as discussed in the 

following sub-sections. 

 

• Objective QoE Measuring Techniques, based 

on network related parameters that need to be 

gathered to predict the users’ satisfaction. 

 

• Subjective QoE Measuring Techniques based 

on surveys, interviews and statistical sampling 

of users and customers to analyze their 

perceptions and needs with respect to the 

service and network quality.  

 

• Hybrid QoE Measuring Techniques, with an 

objective measuring for identifying the 

parameters that have an impact on the 

perceived quality for a sample video database. 

Then the subjective measurement takes place 

through asking a panel of humans to 

subjectively evaluate the QoE while varying 

the objective parameters values. The method 

presented in [11] proposes a QoE measure 

with automatic QoE tool for SVC video 

coding mechanisms. The proposed module is 

based on PSQA (Pseudo Subjective Quality 

Assessment tool), which is a hybrid QoE 

measure technique (objective/subjective) 

assessment tool. PSQA uses RNN (Random 

Neural Network) to capture the non-linear 

relation between the video coding as well as 

the network parameters affecting the video 

quality, and QoE. 

 

 

 

Figure 2: QoE Process & Model  

 

 

 

4. Global QoE in the domestic sphere 

If we take as example a family in the evening that is 

distributed in several rooms of a house with different 

access networks (optical and wireless).  The family 

members watch different content on different kinds of 

devices ranging from high definition TVs to low 

resolution smartphones. If we evaluate the context of 

each user and calculate his best achievable contextual 

QoE, we will be able to know the adequate parameters 

for all the distribution chain. When we consider the 

QoE as a whole and try to optimize it, the context will 

be of great value to us. For example, if a user is using a 

low resolution device, we know that the MoS will not 

 

improve if we increase network parameters such as 

bandwidth (it is useless hence to give more capacity). 

This delta bandwidth can be beneficial to another 

family member for whom the MoS will increase if we 

transfer the bandwidth to his session. So the resulting 

architecture is depicted in the figure.  

 

 

Figure 3: The Global contextual QoE 

 

 

The figure does not show the back loop network 

control for clarity. The optimization process can be 

based on simple linear programming techniques. It 

could be also based on a simple game to find an 

optimal equilibrium point for bandwidth shares versus 

MoS. This is one of the axes that we try to deepen in 

our future studies. The following equation summarizes 

the evaluation process. We calculate the global 

 

Satisfaction (MoS) S�������� based on single MoS 

satisfaction that is function of context information for 

user (i) Ci. 

The global QoE in the domestic sphere is measured by 

this method:  

��

����

�   =  ∑ 

�������   

 

 

N is the number of users and S� is the user satisfaction. 

Ci is a complex function of different context 

parameters. 

 

 

5. Conclusion and Perspectives 

 

Quality of Experience (QoE) becomes crucial for 

service providers and network operators to continue 

gaining users’ satisfaction. It needs to be analyzed in 

the specific context of the client and has to be 

compared with large numbers of customers sharing the 

same resource. This short paper presents a study on the 

QoE measuring means considering both the classical 

methods and the research contributions. We present a 

new approach for QoE with extended context 

information (network context, device context, user 

context, content context…). It  aims at improving the 

user satisfaction. We also argue that global QoE should 

be used for groups sharing a resource to optimize the 

overall distribution parameters.  

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

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The perspectives of this work are to analyze in detail 

the complete distribution chain of multimedia content 

(device, access network, content network) to identify 

which parameters should be first adjusted to have an 

immediate influence on the QoE improvment. 

 

 

 

References 

[1]   M. Diallo, H. Moustafa,  H. Afifi. Quality of experience 

 

for audio-visual services. UP-TO-US '12 Workshop : 

User-Centric Personalized TV ubiquitOus and secUre 

Services, ACM, Berlin, Germany. Vol. 2. pp. 121-127. 

04-06 july 2012. 

 

[2]  T.Tominaga, T.Hayashi, J.Okamoto, A.Takahashi, 

“Performance comparison of subjective quality 

assessment methods for mobile video”, Quality of 

Multimedia Experience (QoMEX), Second International 

Workshop on Date of Conference:  June 2010. 

 

[3]  Song Songbo, H. Moustafa, H. Afifi. Advanced IPTV 

Services Personalization Through Context-Aware 

Content Recommendation. IEEE Transactions on 

Multimedia. Volume: 14 , Issue: 6. December 2012. 

 

[4]  www.conviva.com  

[5]  www.skytide.com  

[6]  www.mediamelon.com 

[7]  M Tourad & Al. “Adaptation of Audio-Visual Contents 

 

and their Delivery Means” Accepted to appear in 

Communications of the ACM. 2013. 

 

[8]  V. George Mathew. (2001) Environmental Psychology. 

http://www.psychology4all.com/environmentalpsycholog

y.htm. 

 

[9]  Deliverable D3 1 1 Context-Awareness- Version15 Dec 

2011.pdf, Task 3.1 (T3.1): Context Definition, Gathering 

and Monitoring. CELTIC UP2US Project. 

 

[10]  I. Martinez-Yelmo, I.Seoane, C.Guerrero, “Fair Quality 

of Experience (QoE) Measurements Related with 

Networking Technologies”, 2010. 

 

[11]  Kamal Deep Singh, Adlen Ksentini ,Baptiste Marienval, 

Quality of Experience measurement tool for  SVC video 

coding, IEEE Communications, 2010.  

 

[12]  I. Politis, M. Tsagkaropoulos, T. Dagiuklas, S. 

Kotsopoulos and P. Stavroulakis, "On the QoS 

Assessment of Video Sessions in Heterogeneous 3G-

WLAN Networks with Seamless and Secure Mobility 

Support ", China Communications Magazine, Special 

Issue on Communications and Information Security, vol. 

4, no.1, pp. 105-119, February 2007. 

 

 

 

Mamadou Tourad Diallo received his M.Sc. from 

Paris University 6 in 2011. He is currently working on 

his Ph.D. within Orange Labs. His thesis work 

concerns new video content networks and context 

adaptation. 

 

Hassnaa Moustafa received her B.Sc. from 

Alexandria University and Ph.D. from ENST Paris. 

She is now with Intel Labs USA.  

 

Hossam Afifi is professor at Institut Mines Télécom, 

Saclay, France. He works on multimedia services and 

applications. 

 

Nicolas Marechal received his Ph.D. from Rennes 

University. He works as senior engineer at Orange 

Labs Issy les Moulineaux. 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

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No-reference IPTV Video Quality Assessment Based on End-to-End Visual Distortion 

 

Estimation  

 

Ning Liao and Zhibo Chen 

 

Technicolor Research & Innovation, Beijing, China 

ning.liao@technicolor.com, chenzb@ieee.org 

 

1. Introduction 

 

No-reference H.264 video quality assessment models 

for IPTV scenario and mobile streaming scenario are 

studied in ITU-T P.NBAMS [1] (Non-intrusive 

Bitstream model for the Assessment of performance of 

Multimedia Streaming) work group. The parsing-mode 

P.NBAMS models do not completely decode the H.264 

video stream; any kind of analysis of the bitstream, 

without using pixel information, can be done. On-line 

video quality monitoring, e.g. at gateway or setup box, 

is a major target application of P.NBAMS model. Thus, 

both prediction accuracy and algorithm complexity are 

important aspects to evaluate a model. Test conditions 

of P.NBAMS databases [2] are designed to reflect the 

realistic application situations, which includes 

compression artifacts introduced due to lossy video 

compression and slicing/freezing artifacts [3] 

introduced due to lossy transmission and different 

Packet Loss Concealment (PLC) method used. 

 

We developed a complete solution for parsing-mode 

P.NBAMS in IPTV scenario and Mobile video 

streaming scenario, which demonstrated the best 

performance in ITU-T P.NBAMS standard competition. 

In this model, the visual distortions of three types of 

above-mentioned artifacts are modeled separately, then 

the mutual influence of perceptible compression 

artifacts and slicing/freezing artifacts is modeled by 

linearly combining the output of the two worst quality 

levels of the individual artifact models. The idea is that 

the overall quality is determined mainly by the worst 

artifact type, regardless of specific artifact types. 

 

In this paper, we only present our quality assessment 

proposal named E2EVD (End-to-End Visual 

Distortion) for slicing artifacts, which is more 

challenging to estimate compared with coding and 

freezing artifacts. One challenge is to evaluate the 

concealed artifacts without the explicit knowledge of 

the pixel signal of the artifacts, which is available only 

after decoding and applying PLC strategy to the lost 

macro blocks (MBs) at decoder. The effectiveness of a 

PLC strategy depends heavily on video content 

characteristics.  

 

In our previous packet-layer model [4], we 

demonstrated that visibility estimation of lost frames 

based on video complexity can significantly improved 

prediction accuracy of video quality, as compared with 

 

PLR. We also demonstrated that considering error 

propagation effects can further improve model 

performance. In E2EVD proposal, the visibility level of 

lost packets is estimated at MB level with more 

accurate content features extracted from video 

bitstream, and scene change detection methods are 

proposed to improve the video quality prediction 

accuracy. The E2EVD scheme demonstrates 

statistically significantly better performance for both 

IPTV (i.e. High Resolution, HR) scenario and mobile 

video stream (i.e. Low Resolution, LR) scenario.  

 

 

2. Description of E2EVD 

 

E2EVD model is shown in Figure 1. Generally, the 

goal of PLC is to estimate lost MBs in order to 

minimize perceptual quality degradation. Visual 

artifacts may still be perceived after PLC, because PLC 

may be not effective therein.  Such visual artifacts 

caused by lost MB are denoted as initial visible 

artifacts.  If a block having initial visible artifacts is 

used as a reference, for example, for intra prediction or 

inter prediction, the initial visible artifacts may 

propagate spatially or temporally to other macro blocks 

in the same or other frames through prediction.  The 

overall visible artifact of a MB is caused by initial 

and/or propagated visible artifacts. Finally, all MBs’ 

visual artifacts in the sequence are aggregated and 

mapped to a numeric video quality index in 1-5 scale. 

 

 

Fig. 1. Block diagram of E2EVD model. 

 

 

 

Initial visible artifact estimation in a scene 

 

The perceived strength of artifacts produced by 

transmission errors depends heavily on the employed 

PLC techniques.  For example, if a frame far away 

from a current frame is used to conceal a current macro 

block, the concealed macro block is more likely to 

have visible artifacts.  So, the distance ������ , in 

 

 

 

 

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display order, between the to-be-concealed frame and 

the concealing frame is a parameter for modeling. In 

addition, the artifact strength is also related to the video 

content.  For example, a slow moving video is easier to 

be concealed.  Thus, parameters, such as motion 

vectors and error concealment distance, can be used to 

assess the error concealment effectiveness and the 

quality of concealed video at a bitstream level. In HR 

scenario, the initial artifacts visibility level for a lost 

MB indexed by (i ,j) of frame n is given by � !"#$#%�&, �, (� = *�+!$,#,, ∗ ������/4.0�                     

*�2� = 345,, 2 < 75�89:8;�<9:<; ∗ �2 − 75�, 75 ≤ 2 ≤ 7?4?, 2 > 7? 45 = 0,4? = 100;75 = 1and7? = 8 in the unit of pixel.  

In LR scenario, in smooth areas of some video 

sequences, for example, in sky and grassland which are 

usually easy to be concealed, unlike in HR scenario, 

the estimated motion vectors in H.264 encoding may 

be large even the movement between pictures are small. 

Consequently, a video quality measurement based on 

motion vectors may falsely estimate strong visible 

artifacts even though the concealed areas have good 

visual quality.  By contrast, the energy of prediction 

residual signal in the smooth areas may be relatively 

small and may provide better indication about the 

 

perceived visual quality.  Thus, residual energy G��$,#,, 

is used as another parameter in estimating the artifact 

level, i.e.,  � !"#$#%�&, �, (� = min{* K+!$,#,, ∗ ������4.0 L , *MG��$,#,,N} 

For scaling of residual signal, 75 = 1, and 7? = 64.   

 

 

Scene cut artifact estimation 

When there is a significant scene change between two 

adjacent pictures and packet loss occurs in the second 

picture of the two adjacent pictures, the concealed 

second picture will have very strong visible artifacts. 

Scene cut artifacts occur at partially received scene cut 

frames or at frames referring to lost scene cut frames. 

The idea behind detecting scene cut frame is that the 

prediction residual energy change or motion change 

around a scene change is often greater. For the lost 

MBs in a detected scene cut frame, set its initial visible 

artifact to a larger value, i.e. 100. 

 

Note that, when one scene changes gradually to another 

scene, and if packet loss occurs in a gradual transition 

picture, the artifacts in the error concealed picture are 

less visible. This is quite contrary to scene cut artifacts. 

However, the energy or motion difference of two 

gradually changed scenes may also be great. Thus, it is 

also important to differentiate the gradual scene change 

from significant scene change.  

 

 

Propagated visible artifacts 

How the artifact level propagates can be traced through 

motion vectors. Experiment shows that it is sufficient 

to use zero motion vectors instead of accurate motion 

vectors to roughly track the temporal propagation of 

visible artifacts. The overall artifacts in a MB should 

consider both initial artifacts and propagated artifacts: � !"�&, �, (� = QR2�� !"�& − S, �, (�,� !"#$#%�&, �, (��

where � !"�& − S, �, (�is the propagated visible artifact 

from reference frame n-k. 

 

 

Spatio-temporal pooling 

 

Finally, sequence-level artifacts are aggregated from 

 

frames’ artifacts LoVA�n�  by a log function, and 

mapped to a MOS by 2nd-order polynomial fitting.  � !"XYZ = [ \5]M�∑ � !"�&�$ � _^`X⁄ + 1N            

cX = �5 × � !"XYZ

 

? + �? × � !"XYZ + �e                  

 

where Fghi  is frame rate, the parameters C5, C?, Ce  are 

 

trained on selected samples that are dominated by 

perceptible slicing artifacts, i.e., the influence of coding 

artifacts on perceptual quality can almost be ignored. 

 

 

3. Performance analysis 

 

In HR case, compared with the full-reference metric 

MSE on five standard defined video databases, no-

reference E2EVD scheme achieved better performance 

with an average correlation of 0.83 with subjective 

score, when tested on slicing samples without 

perceptible coding artifacts. Interestingly, the MSE 

considering slicing artifacts only outperforms the MSE 

equally counting in both coding and slicing artifacts by 

a large margin on some databases. This illustrates that, 

the overall quality is determined by the dominant 

visual distortion; discriminative treatment of signal 

difference caused by different types of artifacts may 

bring significant performance gain.  

 

Using our complete solution, the average RMSE for 

video packet loss conditions causing slicing artifact is 

about 0.4 on 5 scales in HR case, and around 0.5 in LR 

case. 

 

References 

[1] ITU-T Q14/12, “P.NBAMS Terms of Reference,” Jan. 

 

2011.  

[2] ITU-T Q14/12, “P.NAMS and P.NBAMS Test Plan,” 

 

Sept. 2011. 

 

[3] ITU-T Q14/12, “Draft recommendation P。1202”, Sept. 

 

2012. 

[4] N. Liao, ZB Chen, “A Packet-Layer Video Quality 

 

Assessment Model with Spatiotemporal Complexity 

Estimation,” EURASIP Journal on Image and Video 

Processing, 2011 

 

[5] N. Liao, ZB Chen, “No-reference IPTV video quality 

assessment based on end-to-end visual distortion 

estimation”, ICASSP, 2013, submitted. 

 

 

 

 

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Ning Liao is with Research & 

Innovation Dept. of Technicolor, 

Beijing, China.  She received the 

B.E. degree in wireless 

communication and the Ph.D. 

degree in Telecommunication 

Engineering from the Beijing 

University of Posts & 

Telecommunications, China, in 

1998, and in 2007 respectively.  

 

From 1998 to 2001, she was a network engineer in 

China Telecom. She worked on scalable video 

compression, error resilient video coding and 

transmission, and power-efficient service scheduling in 

WiMAX network from 2003 to 2008. Her research 

interests are in the areas of video quality assessment, 

video coding, and wireless multimedia 

communications.    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Zhibo Chen (M’01–SM’11, 

chenzb@ieee.org) received his B. Sc., 

and Ph.D. from EE Tsinghua 

University. He has been with 

Technicolor since 2004 and worked in 

Sony Research before that.  He is 

principal scientist in Technicolor 

Research & Innovation Department, 

Distinguished Fellow of Technicolor 

fellowship program, manager of 

 

media processing lab in Technicolor. His areas of expertise 

and interests include: media processing and coding, media 

Quality of Experience analysis and management for content 

delivery, perceptual based rendering, etc.  He was the Media 

QoE lead for Technicolor Research & Innovation, 

contributed to the design of media QoE assessment platforms 

for CE applications and corresponding ITU-T 

standardization. He has more than 100 granted and filed EU 

and US patent applications, more than 60 publications and 

standard proposals. He is IEEE senior member, member of 

IEEE Visual Signal Processing and Communications 

Committee, and member of IEEE Multimedia 

Communication Committee. He was RC member of ISCAS 

meetings from 2007 to 2013, key member in IEEE MMTC 

special interest group on QoE. He was TPC member of PCS 

and VCIP, Chair of ICME 2011 Multimedia track. He also 

served as co-Editor of IEEE Journal on Selected Areas in 

Communications QoE-Aware Wireless Multimedia Systems 

2011, member of best paper selection committee in IEEE 

VCIP 2012. 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

http://www.comsoc.org/~mmc/ 15/44 Vol.8, No.2, March 2013 

 

 

Video Quality as a Driver for Traffic Management with Multiple Subscriber Classes 

 

Martín Varela and Janne Seppänen 

 

VTT Technical Research Centre of Finland 

 

{martin.varela, janne.seppanen}@vtt.fi

 

1. Introduction 

 

 

 

Video services currently account for a very large 

portion of the total traffic on the Internet, and this 

portion is foreseen to keep rising [1].  This trend, 

coupled with the resource-hungry nature of video 

services, poses significant problems for network 

management, if good perceptual quality levels are to be 

achieved. In mobile networks, in particular, this can be 

a problem when a cell contains several users streaming 

video concurrently. In this paper we present a short 

overview of a multi-faceted mechanism for cross-layer 

quality-driven traffic management for video services at 

the last hop, which we have proposed in [2]. We 

consider over-the-top (OTT) services, where the 

network operator does neither control the content nor 

profit directly from it. Despite ever-more-efficient 

encoding schemes, mobile video traffic is poised to 

keep increasing its need for resources, as high-

resolution displays appear in mobile devices, and users 

become accustomed to HD video on their TV and 

desktop/laptop systems. Since bad quality might lead to 

user churn, solutions in the form of access control, or 

Differentiated Services, have been explored, which 

may allow implementing network QoS mechanisms 

that result in better QoE for the end users. An 

immediate problem that appears in this context is that 

of identifying the traffic to mark as high-priority. In the 

case of RTP-based streams, simply looking at packet 

streams might be sufficient, but with the majority of 

OTT services being HTTP-based, the problem becomes 

non-trivial.  

 Research on quality-driven traffic 

management for video services has been done for IPTV 

(e.g. in [3] [4]), and to a lesser extent on OTT services 

[5] [6] in wireless contexts.  

 

 

2. A multi-faceted approach 

 

 

In this work, we propose a composite approach to 

managing the traffic in order to provide adequate QoE 

to the users. We propose a subscriber-based 

differentiation scheme (implemented, without loss of 

generality, with two classes of users, namely premium 

and normal), and a traffic management scheme based 

on both access control and application-based traffic 

differentiation.  

 Overall, our solution works as follows.  New 

flows arriving at a generic Access Point are classified 

both by their subscriber class and by their application 

 

type (the latter classification is done by using the two-

stage statistical classifier described in [7]). Regardless 

of subscriber class, inelastic flows are only admitted if 

the average Mean Opinion Score (MOS) of other video 

streams is above a set threshold (note that premium 

users cannot preempt normal users, and so a premium 

user’s stream might be dropped even if normal users 

are currently streaming video). If a flow is admitted, 

then it is assigned to a queue with the adequate priority 

based on its application type, subscriber class, and 

current estimated QoE. The objective of this process is 

to ensure that a) admitted video streams provide 

acceptable quality, b) premium users’ streams achieve 

better quality when congestion arises, and c) the system 

is fair to normal users as well (not preempting them, 

and interleaving the priority of premium and normal 

users’ application classes).  

 All flows enter a single FIFO queue, and 

individual flows are promoted on an as-needed basis to 

higher priority queues depending on their current 

quality, subscriber class, and application class. In 

particular, a threshold of 3.0 in the usual 5-point MOS 

DC scale is set, so that actions are taken when a flow’s 

quality estimation drops below this value. A hysteresis 

mechanism is then implemented, ensuring that the 

improved quality achieved by promoting the flow is 

stable for a set period of time over a second threshold 

(4.0 points in the case of the reported results) before 

returning the flow to a lower-priority class (if possible). 

Queues of higher priority are emptied before those of 

lower priority (up to a certain limit), and traffic within 

each queue is handled with stochastic fairness queuing 

[8].  

 The quality estimations were performed using 

a PSQA [9] based model for IPTV-like services.  The 

traffic management was implemented on top of a 

Linux-based router, by using the Hierarchical Token 

Bucket (HTB) queuing discipline [10]. HTB is rather 

complex, but provides a very flexible approach to 

handling different traffic classes. The system uses both 

the priority of a class and a set limit for each class to 

achieve fairness. Within each class’ queue, stochastic 

fairness queuing (as implemented in Linux [11]) is 

used. 

 

 

3. Performance evaluation 

 

 

The performance of the proposed approach was tested 

as a proof-of-concept in a laboratory environment. Four 

different aspects of the proposed system’s performance 

 

 

 

 

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were tested, namely 1) responsiveness in the case of 

congestion (application differentiation), 2) subscriber 

priority handling, 3) reaction times, and 4) admission 

control. All tests were done between 15 and 40 times, 

and the results presented herein are representative of 

the average behavior of the system. 

 In the first set of tests, a test video stream was 

subjected to contention by a large bulk transfer.  Figure 

1 shows the results of the first test 

set.

 

 

Figure 1 - Application differentiation test 

 

 

 

Run #1 was performed without the application-based 

priority handling, whereas run #2 was performed with 

it.  The dashed vertical lines indicate the start of the 

video and bulk streams, respectively at 10s and 60s. It 

is clear from Figure 1 that with the traffic control off, 

the video quality quickly turns unacceptable, while 

when it is on, the quality remains at acceptable levels.  

 

The second set of tests involved two video 

streams belonging to different subscriber classes. The 

link bandwidth was set so that one flow could be 

served without problems, but two flows would congest 

it. Figure 2 shows the results obtained. In the first run, 

the quality of both streams suffers, as expected, since 

the link cannot support both at their peak rates (Figure 

2a).  Figure 2b shows the effect of the subscriber class 

differentiation at work, and it is easy to see that the 

premium user enjoys a significantly better quality than 

the normal user. The reader may notice that there is a 

period in which the premium user will also suffer from 

a lower quality in a first instance in run #2. This is due 

to a trade-off between the size of the time window over 

which the MOS is estimated, and the estimation’s 

accuracy. In practical use, a smaller window would 

probably be useful to avoid the user stopping the 

streaming due to the lowered quality. 

 This leads us to the thirds test set, regarding 

the reaction times of the system. The fastest 

performance achieved resulted in flows being 

promoted to a higher-priority class in 2.8s on average 

over 40 test runs (recall that all flows start out in the 

same FIFO queue by default if there’s no contention). 

The total reaction time was of 4.0s. As mentioned 

before, however, these smaller values impose a trade-

off in the QoS calculations, which become noisier as a 

 

consequence of having fewer samples.  

 

 

 

 

Figure 2 - Subscriber differentiation test 

 

 

 

 The final set of tests was related to the 

admission control. The QoE of the flows in the system 

was averaged over a 30s sliding window, in order to 

avoid noise in the measurements. In this setup, two 

streams were started at different times, and the link 

bandwidth was set low, so that the quality of the first 

stream was acceptable, but not good.  

 

  

 

 

Figure 3 – Admission control test 

 

 

In Figure 3, we can see that in run #1, when the 

admission control is not enabled, the start of the second 

stream results in a completely unacceptable quality for 

both streams. Note that actual degradation is sharper 

than it appears in the plot, as the plot is smoothed by 

the 30s averaging window. In run #2, with admission 

control enabled, when the second flow starts it is 

immediately dropped, as the quality of the first flow is 

below the activation threshold. Thus, the user watching 

that stream attains an acceptable quality throughout the 

whole period.  

 

 

4. Conclusions  

 

 

 

We have proposed a multi-faceted approach to QoE-

based traffic control by considering different subscriber 

and application types and using them to perform 

admission control and traffic differentiation. The 

results obtained show a clear QoE improvement for 

OTT video streams when the proposed mechanisms are 

in place instead of a simple best-effort policy. Further 

work on this subject includes the extension and 

 

 

 

 

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refinement of the traffic classification mechanism used 

to work on adaptive HTTP-based video streaming 

schemes, as well as the development of suitable 

parametric QoE models for them. 

 

References 

[1] Cisco Systems, Inc., "Cisco Visual Networking 

 

Index: Forecast and Methodology, 2011-2016," 

May 2012. 

 

[2] Janne Seppänen and Martín Varela, "QoE-Driven 

Network Management for Real-Time Over-the-

Top Multimedia Services," in IEEE WCNC, 

Shanghai, 2013, Accepted for publication. 

 

[3] D. Waring, G. Lapiotis, J. Lyles, and R. 

Vaidyanathan K. Kerpez, "IPTV Service 

Assurance," IEEE Communications Magazine, vol. 

44, no. 9, pp. 166-172, September 2006. 

 

[4] M. Garcia, M. Atenas, and A. Canovas J. Lloret, 

"A QoE management system to improve the IPTV 

network," International Journal of Communication 

Systems, vol. 24, no. 1, pp. 118-138, 2011. 

 

[5] K. Haugene and A. Jacobsen, "Network based 

QoE optimization for ”over the top” services," 

Norwegian University of Science and Technology, 

Department of Telematics, 2011. 

 

[6] A. Ksentini, C. Viho, and J.-M. Bonnin K. 

Piamrat, "QoE-aware admission control for 

multimedia applications in IEEE 802.11 wireless 

networks," in IEEE VTS - Vehicular Technology 

Conference (Fall), 2008, pp. 1-5. 

 

[7] M. Hirvonen, "Two-phased network traffic 

classification method for quality of service 

management," University of Oulu, Dpt. of 

Electrical and Information Engineering, 2009. 

 

[8] P. E. McKenney, "Stochastic Fairness Queueing," 

in IEEE INFOCOM'90, vol. 2, 1990, pp. 733-740. 

 

[9] Martín Varela, "Pseudo-subjective Quality 

Assessment of Multimedia Streams and its 

 

Applications in Control," Université de Rennes 1, 

Doctoral Thesis Nov. 2005. 

 

[10] M. Devera. (2003) HTB Home. [Online]. 

http://luxik.cdi.cz/~devik/qos/htb/ 

 

[11] B. Hubert. Linux Advanced Routing & Traffic 

Control HOWTO. [Online]. 

http://lartc.org/lartc.html#LARTC.SFQ 

 

 

Dr. Martín Varela 

 

received his PhD and MSc 

from the University of 

Rennes 1 (Rennes, France), 

in 2005 and 2002 

respectively. He has been 

an ERCIM fellow, and 

spent time at SICS and 

VTT, where he has been a 

Senior Scientist since 2007. 

His research interests lie in 

 

the QoE domain, both in QoE for real-time multimedia 

services and for generic services.  

 

 

 

Janne Seppänen received 

his Bachelor's Degree in 

University of Oulu in 2010 

and his Master's Degree 

two years later. In his 

studies, he specialized in 

computer engineering and 

signal processing. He 

currently works at 

Technical Research Centre 

of Finland (VTT) as a 

 

research scientist, covering topics such as Quality of 

Experience, network management, traffic classification 

and network traffic measurement. 

 

  

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

http://www.comsoc.org/~mmc/ 18/44 Vol.3, No.2, March  2013 

 

 

Cross-layer Design for Quality-Driven Multi-user Multimedia  

 

Transmission in Mobile Networks 

 

Maria G. Martini 

 

Kingston University London 

 

{m.martini}@kingston.ac.uk

 

1. Introduction 

 

A critical problem in next generation wireless 

multimedia networks is how to efficiently ensure good 

quality video streaming over a multiple access wireless 

channel with shared communications resources. The 

main aim of achieving a satisfactory Quality of 

Experience (QoE) for the users of the system can be 

afforded at different layers of the protocol stack. 

Dynamic rate control strategies optimized across the 

users can be considered at the application layer in order 

to allocate the available resources according to users’ 

requirements and transmission conditions. Rate control 

was originally adopted with the goal of achieving a 

constant bit-rate, then with the goal to adapt the source 

data to the available bandwidth [1]. Dynamic 

adaptation to variable channel and network conditions, 

i.e., by exploiting the time-varying information about 

lower layers, can be adopted.   

Packet scheduling schemes across multiple users can 

be considered below in the protocol stack (MAC layer) 

in order to adapt each stream to the available resources 

[2]. Content-aware scheduling can also be considered, 

as in [3]. 

At the physical layer, adaptive modulation and coding 

(AMC) can be exploited to improve the system 

performance, by adapting the relevant parameters to 

both the channel and the source characteristics. 

 

 

2. Cross-layer design strategies 

 

Cross-layer design (CLD) solutions should be 

investigated in order to optimize the global system 

based on a Quality of Experience criterion. As an 

example, in [4] a cross layer design approach is 

considered with multiuser diversity which explores 

source and channel heterogeneity for different users. 

Typically cross-layer design is performed by jointly 

designing two layers [5] - [9]. In [9] cross layer design 

takes the form of a network-aware joint source and 

channel coding approach, where source coding - at the 

application layer - and channel coding and modulation 

- at the physical layer - are jointly designed by taking 

the impact of the network into account.  In [7] cross-

layer optimization also involves two layers, application 

layer and MAC layer of a radio communications 

system. The proposed model for the MAC layer is 

suitable for a transmitter without instantaneous channel 

 

state information (CSI). A way of reducing the amount 

of exchanged control information is considered, by 

emulating the layer behavior in the optimizer based on 

a few model parameters to be exchanged. The 

parameters of the model are determined at the 

corresponding layer and only these model parameters 

are transmitted as control information to the optimizer. 

The latter can tune the model to investigate several 

layer states without the need of exchanging further 

control information with the layer. A significant 

reduction of control information to be transmitted from 

a layer to the optimizer is achieved, at the expense of 

the control information from the optimizer to the layers 

that might slightly increase. 

The work in [6] includes in the analysis MAC-PHY 

and APP layers, presenting as an example a 

MAC/application-layer optimization strategy for video 

transmission over 802.11a wireless LANs based on 

classification 

 

 

3. Cross-layer signaling 

 

Very few contributions consider jointly all the layers of 

the protocol stack. Furthermore, most of the cross-layer 

approaches presented in the literature do not address 

signaling across layers, necessary to pass the needed 

side information and control messages among layers 

even of different network devices. Some mechanisms 

are in use,  for instance for  the exchange of  

information about resource reservation or prioritisation  

among  the different system layers, such as  those 

proposed by IETF for QoS provisioning, namely 

differentiated services (DiffServ) and integrated 

services (IntServ). Their aim was to allow an 

application to reserve resources or a specific service 

level from the interconnecting IP network by mapping 

the user requirements at network protocol level. 

Another example of inter-layer signaling can be found 

in the IEEE 802.11e standard where the QoS 

provisioning is performed between the application and 

the medium access layers. The QoS information 

consisting of the priorities of IP packets, to drop them 

selectively, is not sufficient however as an optimization 

method for multimedia transmission. More detailed 

cross-layer information needs to be delivered in order 

to fully optimize the end-to-end transmission. 

In [11] the authors focus on cross-layer feedback, i.e., 

making information from one layer available to another 

 

 

 

 

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layer of the stack. They highlight the need for a cross-

layer feedback architecture and identify key design 

goals for such architecture. 

A review of existing relevant solutions can be found in 

[10].  One solution proposed  for transferring the 

required controlling information is to extend the 

current protocols such as Internet Protocol version 6 

(IPv6) or Internet Control Message Protocol version 6 

(ICMPv6) through the definition of new options and 

message types, respectively. 

This concept of transmitting cross-layer information 

can be referred as “network transparency” [9]; this  

includes the abstract idea of making the underlying 

network infrastructure almost invisible  to all the 

entities involved in the joint optimization. The 

mentioned transparency solutions are potential 

candidates for transferring the control information 

through both wired and wireless network but they do 

not solve fully the problem of transferring control 

information through the protocol layers from 

application  to physical layer and vice versa. 

Furthermore, they do not propose solutions to use this 

information for end-to-end optimization, which 

requires taking into account all protocol layers and 

particularly applications. In addition, the QoS 

information consisting of the IP packet priorities alone 

is not sufficient for delivering optimization information 

between the layers of source and destination devices. 

Hence more detailed information needs to be delivered 

in order to fully optimize the end-to-end QoS of 

multimedia transmission systems across different 

system layers. Besides efforts in research, 

standardization is also needed in the area and very 

recently standardization groups started addressing this 

issue.  

 

The CONCERTO and the OPTIMIX European 

address(ed) cross-layer design strategies, cross-

information to be exchanged   and the strategies to pass 

such information among the layers in mobile networks. 

In order to control the system parameters based on the 

observed data, two controller units were proposed in 

the OPTIMIX project: one at the application layer 

(APP) and one at the base station (BSC) to control 

lower layers parameters [14] and in particular resource 

allocation among the different users based on the 

(aggregated) multiple feedback.  The two controllers 

operate at different time scales, since more frequent 

updates are possible at the base station controller, and 

rely on different sets of observed parameters. The goal 

of the proposed system is to provide a satisfactory 

quality of experience to video users, hence video 

quality is the major target and evaluation criteria, not 

neglecting efficient bandwidth use, real time 

constraints, robustness and backward compatibility. 

 

This system has been implemented in a realistic 

simulation platform based on OMNET++  to test its 

performance: while most cross-layer methodologies do 

not realistically consider the impact on the performance 

of the signaling overhead and complexity, our 

approach allows performance evaluation and 

comparison of cross layer-methodologies in a realistic 

environment. 

 

 

4. Quality assessment  and utility design 

 

Quality assessment of the received multimedia 

information is crucial for two main purposes: 

 

- Final system performance evaluation; 

 

- “On the fly” system adaptation. 

 

In the first case, the goal is to assess the final quality 

reflecting the subjective quality experienced by the 

users (through subjective tests or objective metrics well 

matching subjective results). In the second case, while 

matching subjective results is also of importance, the 

main requirements are the possibility to calculate the 

video quality metric in real-time and without reference 

to the original transmitted signal. An example of real-

time reduced-reference metrics is [15].  

Utility design is also a key issue in this framework: 

when transmitting multimedia signals to multiple users, 

the trade-off between resource utilization and fairness 

among users has to be addressed. In [13] this was 

addressed by focusing on fairness, targeting at the 

maximization of the minimum weighted quality among 

the different users. In [12] we addressed quality 

fairness by relying on the Nash bargaining solution. 

 

 

5.  Conclusion 

 

In this paper we have briefly presented the main 

aspects of quality-driven design for multimedia 

transmission over mobile networks, highlighting design 

issues and open points. Some recent results have been 

also presented. For further recent works on the topic, 

the reader may also refer to [16]. 

 

 

Acknowledgement 

 

 

 

The author acknowledges EU FP7 for the support 

provided in the framework of the projects OPTIMIX 

and CONCERTO. The colleagues involved in the 

projects are also acknowledged. 

 

 

References 

[1] M. Chen and A. Zakhor, “Rate control for streaming video 

 

over wireless,” IEEE Wireless Communications, vol. 12, 

no. 4, pp. 32–41, 2005. 

 

 

 

 

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[2] D. Jurca and P. Frossard, “Video packet selection and 

scheduling for multipath streaming,” IEEE Trans. on 

Multimedia, vol. 9, no. 3, pp. 629–641, 2007. 

 

[3] P. V. Pahalawatta, R. Berry, T. N. Pappas, and A. K. 

Katsaggelos, “Content-aware resource allocation and 

packet scheduling for video transmission over wireless 

networks,” IEEE JSAC, vol. 25, no. 4, pp. 749–759, 

2007. 

 

[4] G.-M. Su, Z. Han, and K. Liu, “Multiuser cross-layer 

resource allocation for video transmission over wireless 

networks,” IEEE Network, March/April 2006. 

 

[5] G. Dimic, N. D. Sidiropoulos, and R. Zhang, “Medium 

Access Control–Physical Cross-Layer Design”, IEEE 

Signal Processing Magazine, Sept. 2004. 

 

[6] M. van der Schaar, D. S. Turaga and R. Wong, 

“Classification-Based System For Cross-Layer Optimized 

Wireless Video Transmission”, IEEE Trans. on 

Multimedia, Vol. 8, No. 5, October 2006. 

 

[7] A. Saul, S. Khan, G. Auer, W. Kellerer and E. Steinbach, 

“Cross-Layer Optimization Using Model-Based 

Parameter Exchange”,  IEEE Intl. Conference on 

Communications (ICC 2007), Glasgow, UK. June 24-28, 

2007. 

 

[8] Q. Liu, X. Wang, and G. B. Giannakis, “A Cross-Layer 

Scheduling Algorithm With QoS Support in Wireless 

Networks”, IEEE Trans. on Veh. Technology, Vol. 55, 

no. 3, pp. 839-847. 2007. 

 

[9] M. G. Martini, M. Mazzotti, C. Lamy-Bergot, J. Huusko, 

and P. Amon, “Content adaptive network aware joint 

optimization of wireless video transmission,” IEEE 

Communications Magazine, vol. 45, no. 1, pp. 84-90, 

2007. 

 

[10] Q. Wang and M. A. Abu-Rgheff, “Cross-Layer 

Signalling for Next-Generation Wireless Systems”, Proc. 

IEEE WCNC 2003. 

 

[11] V. T. Raisinghani and S. Iyer, “Cross-Layer Feedback 

Architecture for Mobile Device Protocol Stacks”, IEEE 

Comms. Magazine, Jan. 2006. 

 

[12] N. Khan, M. G. Martini, and Z. Bharucha, "Quality-

 

aware Fair Downlink scheduling for scalable video 

 

transmission over LTE systems", IEEE SPAWC 2012, 

 

Cesme, Turkey, June 2012. 

 

[13]  M.G. Martini and V. Tralli, “Video quality based 

 

adaptive wireless video streaming to multiple users”, 

 

IEEE Int. Symp. on Broadband Multimedia Systems and 

 

Broadcasting, 2008. 

 

[14] C. Lamy–Bergot, M. G. Martini, P. Hammes, P.  Amon, 

 

J. Vehkaperä, G. Panza, L. Hanzo, M. Chiani, G. Jeney, 

 

“Optimisation of Multimedia over wireless IP links via 

 

X-layer design”, EUMOB 2008. 

 

[15] C. Hewage and M. G. Martini. "Edge-Based Reduced-

 

Reference Quality Metric for 3-D Video Compression 

 

and Transmission." IEEE Journal of Selected Topics in 

 

Signal Processing, vol.  6, no. 5, pp. 471-482, 2012. 

 

[16] M. G. Martini, C. W. Chen, Z. Chen, T.  Dagiuklas, L. 

 

Sun, and X. Zhu, “Guest Editorial - QoE-Aware Wireless 

 

Multimedia Systems”,  IEEE Journal on Selected Areas 

 

in Communications, vol. 30, no. 7, pp.  1153-1156, 2012. 

 

[17] J. Huusko, J. Vehkapera, P. Amon, C.  Lamy-Bergot, G.  

 

Panza, J.  Peltola, and M.G.  Martini, “Cross-layer 

 

architecture for scalable video transmission in wireless 

 

network”, Signal Processing: Image Communication, vol. 

 

22, no. 3, pp. 317—330, 2007. 

 

 

 

Maria G. Martini Maria G. 

Martini is a Reader (Assoc. 

Prof.) in the Faculty of Science, 

Engineering and Computing in 

Kingston University, London. 

She received the Laurea in 

electronic engineering (summa 

cum laude) from the University 

of Perugia (Italy) in 1998 and 

the Ph.D. in Electronics and 

Computer Science from the 

 

University of Bologna (Italy) in 2002. She has led the 

Wireless Multimedia Networking research group in 

Kingston University in a number of national and 

international research projects, funded by the European 

Commission, UK research councils and international 

industries.  

An IEEE Senior Member, she has served as editor and 

reviewer for international journals (recently lead guest 

editor for the IEEE JSAC special issue on "QoE-aware 

wireless multimedia systems" (2012)) and she 

has been in the organising and programme committee 

of several international conferences (e.g., general chair 

of  ICST/ACM MOBIMEDIA 2009  and  EUMOB 

2008, organizer  of  IEEE Streamcomm (ICME 2011) 

and of the First International Workshop on Cross-Layer 

Operation Aided Multimedia Streaming (IEEE VTC 

2011)). 

Her research interests include QoE-aware wireless 

multimedia networks, cross-layer design, joint source 

and channel coding, 2D/3D error resilient video, 

2D/3D video quality assessment, decision theory, and 

medical applications. She has published extensively 

and she is the inventor of several patents on wireless 

video. 

 

 

 

 

 

 

 

 

 

 

 

  

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

http://www.comsoc.org/~mmc/                       21/44               Vol.8, No.2, March 2013 

 

INDUSTRIAL COLUMN: SPEICAL ISSUE ON  

 

“DYNAMIC ADAPTIVE STREAMING OVER HTTP”  

 

 

Guest Editors: Alex Giladi, Zhenyu Wu, Futurewei Technologies, U.S.A  

 

and Guosen Yue, NEC Laboratories America 

 

 

Several market trends and technology developments 

have resulted in the emergence of “over-the-top” (OTT) 

streaming, which utilizes the Internet as a delivery 

medium.  Hardware capabilities have evolved enough 

to create a wide range of video-capable devices, 

ranging from mobile devices to connected TVs, while 

broadband penetration made high-quality Internet 

streaming viable.  

 

As opposed to the traditional “closed” networks, which 

are completely controlled by the multi-system operator 

(MSO), Internet is a “best effort” environment, where 

bandwidth and latency are constantly changing. In 

particular, network conditions are highly volatile in 

mobile networks. Such volatility makes dynamic 

adaptation to network changes a necessity in order to 

provide a tolerable user experience. 

 

Adaptive streaming has become synonymous with 

HTTP streaming. At first glance, HTTP does not seem 

a good fit for a video transport protocol. After all, real-

time UDP-based streaming has been widely used for 

more than a decade. However, ubiquity and scalability 

of HTTP infrastructure, makes use of HTTP for 

Internet video streaming significantly more attractive 

and more scalable. Firewall penetration is yet another 

factor increasing the attractiveness of HTTP streaming.  

Among others, these factors made HTTP streaming the 

technology of choice for rate-adaptive streaming. The 

ubiquity of HTTP infrastructure made HTTP streaming 

a technology of choice for multiplatform and multi-

screen applications even in operator-owned non-OTT 

scenarios. 

 

Several proprietary technologies, such as Apple HTTP 

Live Streaming, Microsoft Smooth Streaming, have 

emerged as popular adaptive streaming solutions. 

MPEG DASH is a newcomer in this family. It is 

versatile and interoperable standard, developed in 

MPEG and 3GPP with the participation of most major 

players in the market and drawing extensively on the 

experience with existing technologies. DASH is backed 

by a large amount of vendors, and adopted by multiple 

standardization organizations and consortia. In parallel, 

it has been steadily gaining attention in the academia.  

 

 

This special issue of e-letter starts with a brief technical 

introduction to DASH. The second paper by O. Oyman 

describes experimentation with use of DASH over LTE 

networks. As DASH does not define adaptation logic, 

this naturally is one of the most active research areas, 

and the last three papers cover different aspects of it. 

The paper by C. Mueller et. al. addresses the issue of 

fairness in bandwidth allocation -- a problem inherent 

in a client-driven adaptation scheme. The paper by S. 

Zhang et al. describes a different shifting paradigm, 

where both media bit rate and quality are taken into 

account. Lastly, the paper by Y. Reznik adds a 

completely new dimension by using information 

available to a mobile device via its sensors to optimize 

the rate adaptation logic 

 

 

 

Alex Giladi (M’06) is a senior 

architect at FutureWei Technologies 

(Bridgewater NJ), where he works on 

adaptive streaming ecosystem and its 

standardization. Previously he 

worked at Avail-TVN, Digital 

Fountain, and Harmonic Lightwaves. 

 

He is actively involved in MPEG DASH 

standardization since 2010, currently serving as the 

editor of two MPEG standards, Part 4 of MPEG DASH 

and Common Encryption for MPEG-2 TS. Alex holds 

a MSEE degree from Stanford University and BSc 

degree from the Technion, Haifa. 

 

 

Zhenyu Wu (S’99 – M’05) is a senior researcher and 

 

project manager from Media Labs, 

Futurewei Technologies, Bridgewater, 

NJ. Prior to that, he was with 

Thomson Corporate Research, 

Princeton, NJ from 2005 to 2010, and 

a consultant for Siemens Corporate 

Research, Princeton, NJ in the 

 

summers of 2002 and 2003. He has been working in 

the general areas of video/image coding, processing 

and media delivery, and has participated in multiple 

related standards.  He is author or co-author of over 30 

technical publications. He received Ph.D degree in 

electrical engineering from University of Arizona in 

2005, and B.S. and M.S. degrees from Shanghai 

 

 

 

 

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University, Shanghai, China in 1996 and 1998 

respectively.   

 

 

Guosen Yue (S'03-M'04-SM'09)  

received the B.S. degree in physics 

and the M.S. degree in electrical 

engineering from Nanjing 

University, Nanjing, China in 1994 

and 1997, respectively, and the 

Ph.D. degree in electrical 

 

engineering from Texas A&M University, College 

Station, TX, in 2004. Since August 2004, he has been a 

Staff Member with the Mobile Communications and 

Networking Research Department, NEC Laboratories 

America, Princeton, New Jersey, conducting research 

for broadband wireless systems and mobile networks. 

His research interests are in the general areas of 

 

wireless communications and signal processing. Dr. 

Yue now serves as an Associate Editor for the IEEE 

TRANSACTIONS ON WIRELESS 

COMMUNICATIONS. He has served as the Associate 

Editor for RESEARCH LETTERS IN 

COMMUNICATIONS, the Guest Editors for 

EURASIP JOURNAL OF WIRELESS 

COMMUNICATION AND NETWORKING special 

issue on interference management and ELSEVIER 

PHYCOM special issue on signal processing and 

coding. He was the Symposium Co-Chair of IEEE ICC 

2010, the Track Co-Chair of IEEE ICCCN 2008, the 

steering committee member of IEEE RWS 2009, and 

the subcommittee chair of IEEE RWS 2008. He is a 

senior member of the IEEE. 

 

 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

http://www.comsoc.org/~mmc/ 23/44 Vol.8, No.2, March 2013 

 

 

MPEG DASH: A Brief Introduction 

 

Alex Giladi FutureWei Technologies  

 

400 Crossing Blvd., Bridgewater NJ, 08807 

 

Email: alex.giladi@huawei.com 

 

  

 

Abstract 

 The Dynamic Adaptive Streaming over 

 

HTTP (DASH) specification, ISO/IEC 23009-1:2012, 

is the newest addition to the growing number of 

adaptive HTTP streaming systems. Openness, feature 

richness and efficiency are what sets DASH apart, and 

make migration from first generation proprietary 

adaptive systems viable and valuable. 

 

In this letter, we provide a consise technical 

overview of the DASH standard and its emerging 

extensions. An excellent review of the standard and its 

background is provided in [11], while an introduction 

specific to MPEG-2 TS is provided in [12]. An in-

depth review of recommendeded implementation 

practices is provided by MPEG in [2]. 

 

 

 

1  Introduction 

 DASH  [1] defines a manifest format, Media 

 

Presentation Description (MPD), and segment formats 

for ISO Base Media File Format (ISO-BMFF)  [7] and 

MPEG-2 Transport Streams  [6]. 

 

A segment is the minimal individually 

addressable unit of data: it is the entity that can be 

downloaded using URLs advertised via the MPD. One 

example of a media segment is a 4-second part of a live 

broadcast, which starts at playout time 0:42:38 , ends at 

0:42:42, and is available within a 3-min time window. 

Another one is a complete on-demand movie, which is 

available for the whole period this movie is licensed. 

 

A  representation is one of the core concepts 

of DASH. It is defined as a single encoded version of 

the complete asset, or of a subset of its components. A 

typical representation would be e.g. ISO-BMFF 

containing unmultiplexed 2.5 Mbps 720p AVC video, 

and separate ISO-BMFF representations for 96 Kbps 

MPEG-4 AAC audio in different languages. This is the 

structure recommended in DASH264  [10]. Conversely, 

a single transport stream containing video, audio and 

subtitles can be a single multiplexed representation. A 

combined structure is possible: video and English 

audio may be a single multiplexed representation, 

while Spanish and Chinese audio tracks are separate 

unmultiplexed representations. 

 

MPD is an XML document, which advertises 

the available media and provides information needed 

by the client in order to select a representation, make 

adaptation decisions, and retrieve segments from the 

network. MPD is completely independent of segment, 

 

and only signals the properties needed to determine 

whether a representation can be successfully played 

and its functional properties (e.g., whether segments 

start at random access points). MPD uses a hierarchical 

data model to describe the complete presentation. 

 

2  MPD 

Representations are the lowest conceptual 

 

level of the hierarchical data model. At this level, MPD 

signals information such as bandwidth and codecs 

required for successful presentation, as well as ways of 

constructing URLs for accessing segments. Additional 

information can be provided at this level, starting from 

trick mode and random access information to layer and 

view information for scalable and multiview codecs to 

generic schemes which should be supported by a client 

wishing to play a given representation (the latter added 

in  [5]). 

 

DASH provides a very rich and flexible URL 

construction functionality. As opposed to a single 

monolithic per-segment URL list (also possible in 

DASH), it allows dynamic construction of URLs, by 

combining parts of the URL (base URLs) that appear at 

different levels of the hierarchy. As multiple base 

URLs can be used, segments can be requested from 

more than one location. This way, DASH allows path 

diversity, improving performance and fault tolerance. 

 

If short segments are used, an explicit list of 

URLs and byte ranges can reach thousands of elements 

per representation. This is inefficient and wasteful, 

especially in case there is a larger amount of 

representations. DASH allows using predefined 

variables (such as segment number, segment time, etc.) 

and printf-style syntax for on-the-fly construction of 

URLs using templates. Instead of listing all segments 

(e.g., seg_00001.ts, seg_00002.ts, ... , seg_03600.ts), its 

enough to write a single line, seg_$Index%05$.ts, to 

express any number of segments, even if they cannot 

be retrieved at the time the MPD is fetched. Due 

efficiency of templates, DASH264  [10] multi-segment 

representations are required to use templates. 

 

Different representations of the same asset (or 

same component, in the un-multiplexed case) are 

grouped into adaptation sets. All representations within 

an adaptation set will render the same content, and a 

client can switch between them, if it wishes to do so. 

 

An example of an adaptation set would be a 

collection of 10 representations with video encoded in 

different bitrates and resolutions. It is possible to 

 

 

 

 

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switch between each one of these at a segment (or even 

a subsegment) granularity, while presenting same 

content to the viewer. Under some segment-level 

restrictions and time alignment, seamless 

representation switch is possible. 

 

A  period, is a time-limited subset of the 

presentation. All adaptation sets are only valid within 

the period, and there is no guarantee that adaptation 

sets in different periods will contain similar 

representations (in terms of codecs, bitrates, etc.). An 

MPD may contain a single period for the whole 

duration of the asset. It is possible to use periods for ad 

markup, where separate periods are dedicated to parts 

of the asset itself and to each advertisement. 

 

DASH uses a simplified version of XLink in 

order to allow loading parts of the MPD (e.g., periods) 

in real time from a remote location. A simple use case 

for this can be ad insertion, when precise timing of ad 

breaks is known ahead of time, whereas ad servers 

determine the exact ad in real time. 

 

A dynamic MPD can change and will be 

periodically reloaded by the client, while a static MPD 

is valid for the whole presentation. Static MPD’s are a 

good fit for VoD applications, whereas dynamic 

MPD’s are used for live and PVR applications. 

 

3  Segments  

 

3.1  Media segments 

 Media segments are time-bounded parts of a 

 

representation, and approximate segment durations 

appear in the MPD. Segment duration does not have to 

be the same for all segments, though in practice 

segment durations will probably be close to constant 

(e.g., DASH264  [10] uses segments with durations 

within a 25% tolerance margin). 

 

MPD can contain information regarding 

media segments that are unavailable at the time it is 

read by the client, and a client needs to calculate when 

a media segment will be available. 

 

3.2  Index segments 

 Another segment type of major importance is 

 

the index segment. Index segments may appear either 

as side files, or within the media segments, and contain 

timing and random access information. Indexes make 

efficient implementation of random access and trick 

modes, but the concept is useful beyond that – index 

segments can be used for more efficient bitstream 

switching. While indispensible for VoD and PVR type 

of applications, indexing less useful in live cases. 

 

3.3  Bitstream switching 

 Several segment-level and representation-

 

level properties are necessary to implement efficient 

bitstream switching. DASH provides explicit 

functional requirements for these, which are expressed 

 

in the MPD in a format-independent way. Each 

segment format specification has to contain the format-

level restrictions that correspond to these generic 

requirements. 

 

Let us denote media segment i  of 

 

representation R  as )(iSR , its duration as ))(( iSD R . 

Furthermore, let its earliest presentation time be 

 

))(( iSEPT R . EPT corresponds to the earliest 

presentation time of the segment, rather than the time is 

not the time at which a segment can be successfully 

played out at random access. 

 

The key to efficient switching is time 

alingment of segments for all representations within an 

adaptation set. This translates intor a requirement that 

 

for any pair of representation aR  and bR  and segment 

 

i, 1))((1))((<))(( −+− iSDiSEPTiSEPT

b

 

R

b

 

R

a

 

R

 

This, combined with the requirement that a segment 

starts with a random access point of certain types, 

ensures the ability to switch at segment border w/o 

need for overlapped downloads and dual decoding. The 

full definition of random access points is described 

extensively in  [7]. 

 

When indexing is used, it is possible to do 

bitstream switching at a subsegment level as well, if 

similar requirements hold for subsegments. 

 

Most systems require time alignment and 

random access point placement restrictions. In terms of 

video encoding, these restrictions typically translate 

into encodings with matching IDR frames at segment 

borders and closed GOP’s. 

 

4  System model 

A DASH client conceptually consists of an 

 

access client, which is an HTTP client, a media engine, 

which decodes and presents media provided to it, and 

an application, to which the access client passes events. 

The only interfaces defined are the on-the-wire formats 

of the MPD and segments, the rest is left to the 

implementers’ disgression. 

 

 

 

 

Figure  1: DASH system model, as defined in  [4] 

 

Timing behavior of a DASH client is slightly 

more complex than that of earlier technologies. While 

in Apple HLS all segments mentioned in a manifest are 

valid, and a client is always polling for new manifests, 

DASH MPD reduces the polling behavior by defining 

 

 

 

 

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MPD update frequency and allowing explicit 

calculation of segment availability. 

 

A static MPD is always valid, whereas a 

dynamic MPD is valid from the time it was fetched by 

the client, for an explicitly stated refresh period. An 

MPD also has a notion of versioning – it may explicitly 

expose its publication time. 

 

MPD provides the easy means for calculating 

availability time of the earliest segment of a period, 

 

(0)AT . Media segment n  is available starting from 

 

time ))(((0)=)(

1

 

0=

iSDTnT R

 

n

 

iAA ∑

 

+ , for the 

 

duration of the timeshift buffer sTt , the latter being 

 

explicitly stated in the MPD. Availability window size 

has a direct impact on the catch-up TV functionality of 

a DASH deployment. Segment availability time can be 

relied upon by the access client as long as it falls within 

the MPD validity period. 

 

For any representation R  MPD declares 

 

bandwidth RB . MPD also defines a global minimum 

 

buffering time, minBT . An access client will be able to 

 

pass a segment to the media engine after minR BTB ×  

 

bits were downloaded, thus, given a segment starts 

with a random access point, the earliest time segment 

 

n  can be passed to the media engine is 

 

mindA BTnTnT ++ )()( , where )(nTd  stands for the 

 

download time of segment n . In order to minimize the 

delay, a DASH client may want to start the playout 

immediately, however MPD may propose a 

 

presentation delay (as an offset from )(nTA ) in order 

to ensure synchronization between different clients. 

Note that tight synchronization of segment HTTP GET 

requests may create a thundering herd effect, severely 

taxing the infrastructure. 

 

MPD validity and segment availability are 

calculated using absolute (i.e., wall-clock) time. Media 

time is expressed within the segments themselves, and 

in the live case drift can develop between the encoder 

and client clocks. This is addressed at the container 

level, where both MPEG-2 TS and ISO-BMFF provide 

synchronization functionality. 

 

The definitions above are somewhat 

simplified, and an excellent in-depth overview of 

DASH timing behavior is provided in  [13]. 

 

5  Events 

 Events  [4] are a very recent extension to 

 

DASH, added in Amendment 1  [4]. As HTTP is 

stateless and client-driven, “push”-style events can be 

emulated using frequent polls. In current ad insertion 

practice in cable/IPTV systems, upcoming ad breaks 

are signaled 3-8 sec. before their start. Thus a 

 

straightforward poll-based implementation would be 

inefficient, and events were designed to address such 

use cases. 

 

Events are “blobs” with explicit time and 

duration information and application-specific payloads. 

Inband events are small message boxes appearing at 

the beginning of media segments, while MPD events 

are a period-level list of timed elements. DASH defines 

an MPD validity expiration event  [4], which identifies 

the earliest MPD version valid after a given 

presentation time. 

 

Events are a powerful new tool, which is 

unique to DASH. New event types and schemes are 

actively explored now, and we would expect some 

amount of upcoming standardization activity in this 

area. 

 

6  Content Protection 

 DASH is agnostic to digital rights 

 

management (DRM), and supports signaling DRM 

scheme and its properties within the MPD. A DRM 

scheme can be signaled via the ContentProtection 

descriptor, and an opaque value can be passed within it. 

In order to signal a DRM scheme, it is enough to have 

a unique identifier for a given scheme and define the 

meaning of the opaque value (or use a scheme-specific 

namespace instead). 

 

MPEG developed two content protection 

standards, Common Encryption for ISO-BMFF (CENC)  

[8] and Segment Encryption and Authentication  [3]. 

Common encryption standardizes which parts of a 

sample are encrypted, and how encryption metadata is 

signaled within a track. This means that the DRM 

module is responsible for delivering the keys to the 

client, given the encryption metadata in the segment, 

while decryption itself uses standard AES-CTR or 

AES-CBC modes. The CENC framework is extensible 

and can use other encryption algorithms beyond these 

two, if defined. Common Encryption is used with 

several commercial DRM systems, and is the system 

used in DASH264  [10]. 

 

DASH Segment Encryption and 

Authentication (DASH-SEA)  [3] is agnostic to the 

segment format – encryption metadata is passed via the 

MPD, as opposed to the inband mechanisms of CENC  

[8] and traditional MPEG-2 Conditional Access  [6]. 

For example, MPD contains information on which key 

is used for decryption of a given segment, and how to 

obtain this key. The baseline system is equivalent to 

the one defined in HTTP Live Streaming (HLS)  [14], 

with AES-CBC encryption and HTTPS-based key 

transport. This has a side effect of making MPEG-2 TS 

media segments compatible with encrypted HLS 

segments. The standard itself is very extensible, and 

allows other encryption algorithms and more DRM 

systems, similarly to CENC. 

 

 

 

 

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DASH-SEA also offers a segment authenticity 

framework. This frameworks ensures that the segment 

received by the client is same as the one the MPD 

author intended the client to receive. This is being done 

using MAC or digest algorithms, and the intent is to 

prevent content modification within the network (e.g., 

ad replacement, altering inband events, etc.) 

 

 

7  Current Standardization Activities 

MPEG is actively working on exploration in 

 

areas related to DASH. Several core experiments were 

established in 2012-2013, exploring areas such as 

quality-driven streaming, non-HTTP distribution, 

issues related to live and low-delay services, advanced 

uses of events, and more. Activities related to reference 

software and an extensive set of implementation 

guidelines are at their advanced stage. Several 

standardization organizations and consortia adopted 

DASH or are in process of adopting it. 

 

DASH industry forum was established in 

2012. This forum is not an SDO -- it provides a set of 

interoperability guidelines and precisely defined 

interoperability points, DASH264 [10], as well as 

corresponding test material and software tools. The 

first version of DASH264 is at the public review stage, 

with more extensions coming. 

 

8  Summary 

 In this letter we have provided a technical 

 

overview of the MPEG DASH standard and its newer 

extensions. During the 14 months that passed from the 

time it finalized, DASH gaining momentum in the 

industry. More than 50 companies joined the newly 

established DASH Industry Forum, and client 

implementations are already available. In the next 14 

months we hope this interest will result in commercial 

deployments. 

 

 

References 

 

[1]  ISO/IEC 23009-1:2012  Information Technology – 

Dynamic adaptive streaming over HTTP (DASH) – 

 

Part 1: Media presentation description and segment 

 

formats 

 

[2]  ISO/IEC PDTR 23009-3  Information Technology 

– Dynamic adaptive streaming over HTTP (DASH) – 

 

Part 3: Implementation Guidelines 

 

[3]  ISO/IEC FDIS 23009-4  Information Technology – 

Dynamic adaptive streaming over HTTP (DASH) – 

 

Part 4: Segment Encryption and Authentication 

 

[4]  ISO/IEC Study DAM 23009-1  Information 

Technology – Dynamic adaptive streaming over HTTP 

 

(DASH) – Part 1: Media presentation description and 

 

segment formats, Amendment 1: Support for Event 

 

Messages and Extended Audio Channel Configuration 

 

[5]  ISO/IEC COR 23009-1  Information Technology – 

Dynamic adaptive streaming over HTTP (DASH) – 

 

Part 1: Media presentation description and segment 

 

formats, Technical Corrigendum 1 

 

[6]  ITU-T Rec. H.222.0 | ISO/IEC 13818-1:2012, 

Information technology “ Generic coding of moving 

 

pictures and associated audio information: Systems 

 

[7]  ISO/IEC 14496-12,  Information technology 

“ Coding of audio-visual objects “ Part 12: ISO base 

 

media file format (technically identical to ISO/IEC 

 

15444-12) 

 

[8]  ISO/IEC 23001-7,  Information technology 

“ MPEG systems technologies” Part 7: Common 

 

encryption in ISO base media file format files  

 

[9]  WebM Project, “WebM Dash Specification”, 

http://wiki.webmproject.org/adaptive-streaming/webm-

dash-specification 

 

[10]  DASH Industry Forum, “DASH264 

Implementation Guidelines v0.9”, January 2013, 

http://dashif.org/guidelines/DASH264-base-v09.pdf 

 

[11]  I. Sodagar, “The MPEG-DASH Standard for 

Multimedia Streaming Over the Internet,”  IEEE 

Multimedia, Oct-Nov, 2011. 

 

[12]  M. Kar, A. Giladi, “Using DASH and MPEG-2 

TS for Adaptive Multiplatform Delivery,”  SCTE 

Cable-Tec Expo, ,” November 15-17, 2011, Atlanta 

GA. 

 

[13]  T. Stockhammer, “Live Timing for DASH v0.56”, 

DASH-IF, January 14, 2013 

 

[14]  R. Pantos, W. May, “HTTP Live Streaming”, 

http://tools.ietf.org/html/draft-pantos-http-live-

streaming (work in progress) 

 

 

 

 

Alex Giladi (M’06) is a 

senior architect at FutureWei 

Technologies (Bridgewater 

NJ), where he works on 

adaptive streaming ecosystem 

and its standardization. 

Previously he worked at 

Avail-TVN, Digital Fountain, 

and Harmonic Lightwaves. 

 

He is actively involved in MPEG DASH 

standardization since 2010, currently serving as the 

editor of two MPEG standards, Part 4 of MPEG DASH 

and Common Encryption for MPEG-2 TS. Alex holds 

a MSEE degree from Stanford University and BSc 

degree from the Technion, Haifa. 

 

 

 

 

 

 

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Optimizing DASH Delivery Services over Wireless Networks 

 

Ozgur Oyman, Intel Labs, Santa Clara, CA 95054 USA 

 

ozgur.oyman@intel.com 

 

 

1- Introduction on DASH: The growing consumer 

demand for mobile video services is one of the key 

drivers of the evolution of wireless multimedia solutions 

requiring exploration of new ways to optimize future 

wireless networks for video services towards delivering 

enhanced capacity and quality of experience (QoE). One 

of these key video enhancing solutions is HTTP adaptive 

streaming (HAS), which has recently been spreading as a 

form of internet video delivery with the recent 

deployments of proprietary solutions such as Apple HTTP 

Live Streaming, Microsoft Smooth Streaming and Adobe 

HTTP Dynamic Streaming, and is expected to be 

deployed more broadly over the next few years.  

 

In the meantime, the standardization of HTTP Adaptive 

Streaming has also made great progress with the recent 

completion of technical specifications by various 

standards bodies. More specifically, the Dynamic 

Adaptive Streaming over HTTP (DASH) has recently 

been standardized by Moving Picture Experts Group 

(MPEG) and Third Generation Partnership Project 

(3GPP) as a converged format for video streaming [1]-[2], 

and the standard has been adopted by other organizations 

including Digital Living Network Alliance (DLNA), 

Open IPTV Forum (OIPF), Digital Entertainment Content 

Ecosystem (DECE), and Hybrid Broadcast Broadband TV 

(HbbTV). DASH today is endorsed by an ecosystem of 

over 50 member companies at the DASH Industry Forum 

(DASH IF). 

 

 

2- Research Challenges for Optimizing DASH 

 

Deployment in Wireless Networks: As a relatively new 

technology in comparison with traditional streaming 

techniques such as Real-Time Streaming Protocol (RTSP) 

and HTTP progressive download, deployment of DASH 

services presents new technical challenges. In particular, 

enabling optimized end-to-end delivery of DASH services 

over wireless networks requires developing new 

algorithms, architectures, and signaling protocols for 

efficiently managing the limited network resources and 

enhancing service capacity and user QoE. 

 

Such development must also ensure access-specific (e.g., 

3GPP-specific) optimizations for DASH services, which 

clearly require different approaches and methods 

compared to those for traditional streaming techniques, 

observing the client-driven nature of DASH and presence 

of the TCP layer, and that QoE for DASH is measured via 

different performance metrics. The rich set of research 

vectors in this space include: 

 

 

i) Development of evaluation methodologies and 

performance metrics to accurately assess user QoE for 

DASH services (e.g., those adopted as part of MPEG and 

3GPP’s DASH specifications [1, 2]), and utilization of 

these metrics for service provisioning and optimizing 

network adaptation.  

 

ii) DASH-specific QoS delivery and service adaptation at 

the network level, that involves developing new policy 

and charging control (PCC) guidelines, QoS mapping 

rules and resource management techniques over radio 

access network and core IP network architectures,  

 

iii) QoE/QoS-based adaptation schemes for DASH at the 

client, network and server (potentially assisted by QoE 

feedback reporting from clients), to jointly determine the 

best video, transport, network and radio configurations 

toward realizing the highest possible service capacity and 

end user QoE. The broad range of QoE-aware DASH 

optimization problems emerging from this kind of a cross-

layer cooperation framework includes investigation topics 

such as QoE-aware radio resource management and 

scheduling, QoE-aware service differentiation, admission 

control, and QoS prioritization, and QoE-aware 

server/proxy and metadata adaptation.  

 

iv) DASH-specific transport optimizations over 

heterogeneous network environments, where content is 

delivered over multiple access networks such as WWAN 

unicast (e.g., 3GPP packet-switched streaming [3]), 

WWAN broadcast (e.g., 3GPP multimedia broadcast and 

multicast service [4]) and WLAN (e.g., WiFi) 

technologies.  

 

 

3- Evaluation of Service Capacity and QoE for DASH: 

 

This section addresses the first and partly third research 

vectors above for optimizing DASH delivery in wireless 

networks. More specifically, we summarize our proposed 

capacity and QoE evaluation methodology for DASH 

services based on the notion of rebuffering percentage as 

the central indicator of user QoE, and associated 

empirical data based on simulations conducted over 3GPP 

Long Term Evolution (LTE) networks. Further details on 

our work can be found in the papers listed in [5]-[6]. 

 

For our capacity evaluation, we use a dynamic system-

level simulator for the LTE air-interface based on a 

MATLAB-based software platform with suitable 

abstractions of application, transport, MAC and physical 

layers (details in [5]-[6]). We measure video capacity in 

terms of the number of unicast video streams that can be 

 

 

 

 

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simultaneously supported for a given target QoE. The 

QoE metric of interest here is the rebuffering percentage, 

which is defined as the percentage of the total 

presentation time in which the user experiences 

rebuffering due to buffer starvation which has a 

significant impact on the end user quality of experience.  

It is worth noting here that in a recent study conducted by 

Conviva, rebuffering has been identified the single most 

dominating QoE impairment. In particular, our LTE 

capacity evaluation counts the number of users 

simultaneously supported via HTTP-based unicast video 

streaming sessions where the users are “satisfied” Acov 

percentile of the time, with a user being counted as 

satisfied if and only if the rebuffering percentage in its 

video streaming session is less than Aout.  

 

 

Fig. 1: LTE unicast video capacity comparison 

 

 

Fig. 2: Empirical CDF of rebuffering percentage for HTTP 

 

progressive download and DASH-based adaptive streaming 

 

 

Fig. 1 shows the LTE unicast video capacities of HTTP-

based fixed-rate streaming (i.e., progressive download) 

and DASH-based HTTP adaptive streaming, with Acov = 

95% and Aout = 5% subject to different target peak-to-

signal ratio (PSNR) values of 32 dB and 37 dB, 

respectively. For the same set of streaming protocols, Fig. 

2 shows the distribution of rebuffering percentage for the 

37 dB target PSNR case. The capacity-quality tradeoff as 

a function of target PSNR is evident from the empirical 

data, i.e., the LTE system can support much higher 

number of users when the target PSNR is reduced. More 

importantly, the results clearly demonstrate that DASH-

 

based HTTP adaptive streaming allows for supporting a 

significantly larger number of video users in comparison 

with HTTP-based progressive download techniques: With 

fixed rate streaming over LTE at a target PSNR of 37 dB 

and only with 20 users in the system, the 95-th percentile 

value of rebuffering percentage is 5% whereas the 

corresponding value for an LTE system with DASH-

based adaptive streaming and twice as load (i.e., with 40 

users) is less than 1%. This is an intuitively expected 

outcome, given the significantly varying link quality 

among the users in the LTE network, leading to frequent 

occurrences of rebuffering with HTTP-based progressive 

download in the absence of any video quality/bitrate 

adaptation, especially when the network is unable to 

support the fixed bitrate during moments of low 

throughput caused by unfavorable link conditions. In 

contrast, with DASH-based HTTP adaptive streaming, 

each client device can dynamically select the 

quality/bitrate levels of the fetched videos to ensure 

continuous playback while also optimizing quality that 

could be achieved for the given link throughput, and such 

adaptation capability ensures finding the best possible 

compromise between high video quality and minimal 

occurrences of rebuffering events and delivering 

enhanced QoE to a larger number of LTE clients.  

 

REFERENCES 

 

[1] ISO/IEC DIS 23009-1: “Information technology — Dynamic 

adaptive streaming over HTTP (DASH) – Part 1: Media 

presentation description and segment formats” 

[2] 3GPP TS 26.247: “Transparent end-to-end packet switched 

streaming service (PSS); Progressive download and dynamic 

adaptive streaming over HTTP (3GP-DASH)” 

[3] 3GPP TS 26.234: "Transparent end-to-end packet switched 

streaming service (PSS); Protocols and codecs" 

[4] 3GPP TS 26.346: “Multimedia Broadcast Multicast 

Service (MBMS); Protocols and codecs” 

[5] O. Oyman and S. Singh, “Quality of experience for HTTP 

adaptive streaming services,” IEEE Commun. Mag., vol. 50, 

no:4, pp. 20-27, Apr. 2012. 

[6] S. Singh, O. Oyman, A. Papathanassiou, D. Chatterjee and J. 

Andrews, “Video Capacity and QoE Enhancements over LTE”, 

IEEE International Conference on Communications(ICC) 

Workshop on Realizing Advanced Video Optimized Wireless 

Networks, June 2012. 

 

 

 

OZGUR OYMAN (ozgur.oyman 

@intel.com) is a senior research 

scientist and project leader in the 

Wireless Communications Lab of Intel 

Labs. He joined Intel in 2005. He is 

currently in charge of video over 

3GPP Long Term Evolution (LTE) 

research and standardization, with the 

aim of developing end-to-end video 

 

delivery solutions enhancing network capacity and user quality 

of experience (QoE). He also serves as the principal member of 

the Intel delegation responsible for standardization at 3GPP SA4 

 

 

 

 

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Working Group (codecs). Prior to his current roles, he was 

principal investigator for exploratory research projects on 

wireless communications addressing topics such as client 

cooperation, relaying, heterogeneous networking, cognitive 

radios and polar codes. He is author or co-author of over 70 

technical publications, and has won Best Paper Awards at IEEE 

GLOBECOM’07, ISSSTA’08 and CROWNCOM’08. His 

service includes Technical Program Committee Chair roles for 

technical symposia at IEEE WCNC’09, ICC’11, WCNC’12, 

ICCC’12 and WCNC’14. He also serves an editor for the IEEE 

TRANSACTIONS ON COMMUNICATIONS. He holds 

Ph.D. and M.S. degrees from Stanford University, and a B.S. 

degree from Cornell University. 

 

 

 

 

IEEE COMSOC MMTC E-Letter 

 

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Fair Share Dynamic Adaptive Streaming over HTTP 

 

 

Christopher Müeller, Stefan Lederer, and Christian Timmerer 

 

Multimedia Communication (MMC) Research Group, Institute of Information Technology (ITEC) 

 

Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria 

 

{Christopher.Mueller, Stefan.Lederer, Christian.Timmerer}@itec.aau.at 

 

 

Abstract 

 

Multimedia delivery over the Hypertext Transfer 

Protocol (HTTP) is currently very popular and with 

MPEGs' Dynamic Adaptive Streaming over HTTP 

(DASH) a standard is available to provide 

interoperability and enable large-scale deployments 

using existing infrastructures (servers, proxies, caches, 

etc.). This paper identifies some issue when multiple 

DASH clients compete for a bandwidth bottleneck 

when transparent proxy caches are deployed. Therefore, 

we propose a fair share adaptation scheme to be 

included within the client which – through experimental 

results – achieve a more efficient utilization of the 

bottleneck bandwidth and less quality switches. 

 

Index Terms—Dynamic Adaptive Streaming over 

HTTP, DASH, Fair Adaptation, Proxy Cache, 

Multimedia 

 

1. INTRODUCTION 

 

The delivery of multimedia content over-the-top of 

existing infrastructures (servers, networks, caches, 

proxies) using the Hypertext Transfer Protocol (HTTP) 

is gaining more and more momentum despite its being 

designed for best effort and not for real-time 

multimedia transport. The Moving Picture Experts 

Group (MPEG) has recently ratified a standard for the 

Dynamic Adaptive Streaming over HTTP (DASH) [1] 

which is able to handle varying bandwidth conditions 

and allows for flexible deployments over existing 

infrastructures. The basic principle of DASH-based 

multimedia delivery is to (i) provide multiple versions 

of the same content – referred to as representations –, 

(ii) chop the content into time-aligned segments to 

enable seamless switching between different 

representations, and (iii) enable the client to request 

these segments individually based on its current 

conditions. This approach scales very well but it may 

introduce some new drawbacks, as clients are not aware 

of each other and when transparent proxy caches are 

deployed. In particular, consider the use case where 

multiple clients compete for a bottleneck bandwidth 

with a transparent proxy cache involved. Each client 

requests segments based on its own estimated 

throughput that may result in an uneven distribution of 

segments corresponding to different representations. 

 

Thus, clients may switch frequently between 

representations receiving segments alternatingly from 

the proxy cache and origin server respectively. The 

problem is further detailed in [4]. 

 

A similar problem has been identified in [2], TCP 

fairness has been addressed in [3] but without 

considering proxy caches, and a fair share adaptation 

scheme has been proposed in [4]. This paper aims to 

summarize the major findings from [4] and is organized 

as follows. Section 2 describes the fair share adaptation 

scheme for DASH. Section 3 provides experimental 

results while Section 4 concludes this paper and 

provides future work. 

 

 

2. FAIR SHARE DYNAMIC ADAPTIVE 

 

STREAMING OVER HTTP 

 

Our Fair Share Adaptation Scheme (FSAS) aims to 

address the problem identified in Section 1. Therefore, 

we introduce an exponential backoff within the 

adaptation logic of the DASH client. Although this 

approach decreases the number of switch ups to a 

higher representation (after prior switch down) but does 

not consider whether bandwidth fluctuations are either 

caused by the client itself or the network. Self-caused 

frequent switching between representations get 

introduced because the clients' adaptation logic is not 

aware whether segments are received from the proxy 

cache or the origin server. Note that these negative 

effects only occur when a client switches to a higher 

quality level due to a wrong interpretation of the 

throughput estimation. Therefore, we have incorporated 

a probe method to identify the effective available 

bandwidth. The following techniques have been 

identified: 

1. The server provides a non-cacheable object which 

 

guarantees that the client will measure the 

bandwidth to the server. 

 

2. The client downloads the first few bytes or a 

random byte range of the next segment to estimate 

the effective available bandwidth. Typically, most 

proxies do not cache byte range requests. 

 

3. The proxy cache modifies the MPD and removes 

the qualities that could not be served due to 

bandwidth limitations. 

 

 

 

 

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4. The proxy cache offers a service that provides 

information about the effective available 

bandwidth. 

 

We have decided to use method 2 for our system as it 

does not require any changes on the network side and, 

thus, can be easily deployed over existing 

infrastructures. However, any other method will lead to 

the same results. 

 

Algorithm 1. Fair share adaptation algorithm using 

exponential backoff with probe. 

if backoff > 0 

 

backoff := backoff - γ 

endif 

 

quality_level := find 

 

(measured_bandwidth) 

if quality_level > quality_last_segment 

 

if backoff <= 0 

 

if probe(quality_level) 

 

count := 0 

else 

 

backoff := (int) α * e(β * count) 

 

count := count + δ 

 

quality_level := 

 

quality_last_segment 

endelse 

 

else 

 

quality_level := quality_last_segment 

endelse 

 

endif 

 

return quality_level 

 

Algorithm 1 depicts our adaptation logic that returns the 

quality level for the next segment. The backoff could be 

adjusted to the network characteristics with the 

parameters α and β. In our experiments we set them to 1 

for simplicity reasons. Additionally, it is possible to 

accelerate or decelerate the backoff process with the 

parameters γ and δ. Furthermore, this algorithm uses the 

previously mentioned probe method to identify the 

effective available bandwidth for the next segment. 

 

This means that every adaptation decision which leads 

to a switch up will be verified. 

 

 

3. EXPERIMENTAL RESULTS 

 

The architecture of our evaluation network is depicted 

in Figure 1. The proxy and the shaper are both based on 

Ubuntu 10.04. The shaper controls the bandwidth of the 

clients with the Linux traffic control system (tc) and  

the hierarchical token bucket (htb) has been used which 

is a classfull queuing discipline (qdisc). The available 

bandwidth for both clients remains static over the whole 

evaluation, i.e., 1100 Kbps for client 1 and 2200 Kbps 

for client 2. The proxy is based on the Squid [5] in 

transparent mode which also limits the bandwidth to the 

shaper with tc and htb. The evaluation has been 

performed with the Big Buck Bunny sequence at two 

representations with 700 and 1300 Kbps. Please note 

that for this experiment the available bandwidth will not 

change during the whole streaming session as dynamic 

bandwidth conditions may influence the negative 

effects even more. For example, the client makes an 

unfavorable adaptation decision when the network 

bandwidth drops. These evaluations under dynamic 

bandwidth conditions will be part of our future 

research. 

 

 

Figure 1. Experimental setup. 

 

 

Figure 2. MPEG-DASH clients without fair share adaptation scheme. 

 

 

 

 

 

 

 

 

 

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Figure 3. MPEG-DASH clients with fair share adaptation scheme. 

 

 

The evaluation results for MPEG-DASH without our 

fair share adaptation scheme are depicted in Figure 2. 

Figure 2(a) shows the adaptation process, Figure 2(b) 

shows the behavior of the proxy cache and the cache 

hits for each request, and Figure 2(c) shows the buffer 

fill state. While client 1 does not switch to a higher 

representation (due to a max. bandwidth of 1100 Kbps), 

client 2 constantly tries to request a higher 

representation but fails due to the bottleneck bandwidth 

between proxy and server that is shared between both 

clients and, thus, results in frequent quality switches (cf. 

Figure 2(a)). This is behavior is also reflected by the 

cache hits in Figure 2(b). However, both clients ensure 

an almost smooth playback shown in Figure 2(c) except 

around second 380 where the buffer of client 2 is 

almost empty which resulted in stalling. 

 

For MPEG-DASH clients employed with our fair share 

adaptation scheme the number of quality switches is 

reduced significantly as shown in Figure 3(a). The 

green lines indicate the probe points of the algorithm. 

Our approach also increases the cache performance 

depicted in Figure 3(b) and the buffer fill state of both 

clients is always far away from stalling as shown in 

Figure 3(c), thus, smooth playback is ensured by the 

client. 

 

 

4. CONCLUSIONS AND FUTURE WORK 

 

 

This paper identified some issues when multiple DASH 

clients compete for a bottleneck bandwidth with 

transparent proxy caches involved resulting in frequent 

quality switches which leads to a lower user experience 

including stalling. As a solution to this problem we 

proposed a fair share adaptation scheme using an 

exponential backoff algorithm with probing that 

reduces the number of quality switches, increases the 

cache performance, and ensures smooth playback 

without stalling. Our future work comprises the 

evaluation of the fair share adaptation scheme under 

 

dynamic bandwidth conditions with more than two 

clients and competing non-DASH traffic. We assume 

that the negative effects will be increased in such a 

scenario. 

 

 

5. ACKNOWLEDGMENTS 

 

This work was supported in part by the EC in the 

context of ALICANTE (FP7-ICT-248652), 

SocialSensor (FP7- ICT-287975), and partly performed 

in the Lakeside Labs research cluster at AAU. 

 

 

REFERENCES 

 

 

[1] I. Sodagar, “The MPEG-DASH Standard for Multimedia 

 

Streaming Over the Internet”, IEEE MultiMedia, vol.18, 

no.4, pp.62-67, Apr. 2011. 

 

[2] S. Akhshabi, L. Anantakrishnan, C. Dovrolis, A. C. Begen, 

“What Happens when HTTP Adaptive Streaming Players 

Compete for Bandwidth?”, NOSSDAV 2012, Toronto, 

Canada, Jun. 2012. 

 

[3] R. Kuschnig, I. Kofler, and H. Hellwagner, “An 

evaluation of TCP-based rate-control algorithms for 

adaptive internet streaming of H.264/SVC”, ACM 

Multimedia systems (MMSys), Scottsdale, AZ, USA, Feb. 

2010. 

 

[4] C. Mueller, S. Lederer, C. Timmerer, “A proxy effect 

analyis and fair adatpation algorithm for multiple 

competing Dynamic Adaptive Streaming over HTTP 

clients”, IEEE Visual Communications and Image 

Processing (VCIP) 2012, San Diego, CA, USA, Nov. 

2012. 

 

[5] Squid, http://www.squid-cache.org/, (last access: Feb. 

2013). 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Christopher Müller received his 

B.Sc. (Bakk.) in Mar’12 from the 

 

Alpen-Adria-Universität 

Klagenfurt. His research interests 

are multimedia streaming, 

networking, and multimedia 

adaptation; he has published more 

than 10 papers in these areas and 

currently holds one U.S. patent in 

the area of DASH. He gained 

 

practical expertise in various companies (Infineon, Dolby 

Laboratories Inc. LA, etc.) and participated in the MPEG-

DASH standardization, contributed several open source tools 

(VLC plugin, libdash) and participated in several EC-funded 

projects (ALICANTE, SocialSensor). 

  

 

Stefan Lederer holds the position 

of an Assistant Professor at the 

Institute of Information Technology 

(ITEC), Multimedia Communication 

Group. He received his B.Sc. (Bakk.) 

in Business Administration as well 

as his B.Sc. (Bakk.) in Information 

Management in Aug’10 and his 

M.Sc. (Dipl.-Ing.) in Computer 

Science in Mar’12, all from the 

Alpen-Adria-Universität Klagenfurt. 

 

His research topics include transport of modern/rich media, 

multimedia adaptation, QoS/QoE as well as future internet 

 

architectures and he has published more than 10 papers in 

these areas He participated in the MPEG-DASH 

standardization, contributed several open source tools 

(DASHEncoder, DASHoverCCN VLC plugin) and datasets. 

He also participated in several EC-funded projects 

(ALICANTE, SocialSensor). 

  

 

Christian Timmerer is an 

assistant professor in the Institute 

of Information Technology 

(ITEC), Alpen-Adria-Universität 

Klagenfurt, Austria. His research 

interests include immersive 

multimedia communication, 

streaming, adaptation, and 

Quality of Experience. He was 

the general chair of WIAMIS’08, 

ISWM’09, EUMOB’09, 

AVSTP2P’10, WoMAN’11 and 

has participated in several EC-

funded projects, notably 

 

DANAE, ENTHRONE, P2P-Next, ALICANTE, and 

SocialSensor. He also participated in ISO/MPEG work for 

several years, notably in the area of MPEG-21, MPEG-M, 

MPEG-V, and DASH/MMT. He received his PhD in 2006 

from the Alpen-Adria-Universität Klagenfurt. Publications 

and MPEG contributions can be found under 

research.timmerer.com, follow him on twitter.com/timse7, 

and subscribe to his blog blog.timmerer.com.

 

 

 

 

 

 

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Quality Driven Streaming Using MPEG-DASH 

 

Shaobo Zhang, Yangpo Xu, Peiyun Di, Alex Giladi, Changquan Ai, Xin Wang 

 

Huawei Technologies 

 

 

Abstract 

 

Video streaming is becoming more and more popular, 

with video traffic exceeding 50% of the total mobile 

traffic in 2012 according to Cisco VNI. Adaptive 

streaming over HTTP is responsible for a significant 

part of this traffic. Current HTTP streaming systems 

are client-driven – i.e., the client makes decision on 

how to adapt to the constantly changing network 

environment. Currently, such adaptation is based on 

bitrate of encoded content. In many cases, higher 

bitrates do not result in comparable increase in 

perceptual quality.  

In this paper, we propose quality driven streaming – a 

paradigm where adaptation decisions are taken based 

on a combination of bitrate and bandwidth, with 

adaptation performance compared to the one of 

traditional bitrate-only algorithms.  

In this paper, quality-driven streaming was 

implemented as an extension of MPEG DASH, as a 

part of the ongoing MPEG DASH Quality-Driven 

Streaming core experiment. With that said, the concept 

of quality-driven streaming is not DASH-specific and 

can be used with any of the modern HTTP streaming 

systems 

 

Index Terms— DASH, streaming, adaptation, quality 

 

1. INTRODUCTION 

 

Video streaming is becoming more and more popular, 

with video traffic exceeding 50% of the total mobile 

traffic in 2012 according to Cisco VNI [11]. Adaptive 

streaming over HTTP is responsible for a significant 

part of this traffic.  

 

Apple HTTP Live Streaming [2] and Microsoft Smooth 

Streaming [1] are the mostly widely deployed 

proprietary adaptive streaming solutions. A relative 

newcomer to the scene, MPEG Dynamic Adaptive 

Streaming over HTTP (DASH) [3] is an international 

standard which builds on the previous industry 

experience. DASH is gaining traction in the industry, 

and the authors expect it to be widely adopted and 

enable interoperability in multimedia streaming.  

 

Capability to adapt to constantly changing network 

conditions, thus maintaining good user experience, is 

the most fundamental requirement for streaming over 

an open non-provisioned network. Adaptive streaming 

over HTTP is client-driven. An asset is encoded in 

multiple versions, representations in DASH 

terminology-- at multiple bitrates, resolutions, etc., and 

 

broken into discrete segments and (possibly) 

subsegments – addressable and playable parts of an 

asset, typically several seconds long.  

 

Available representations are advertised to a client via 

the MPD (Media Presentation Description), an XML 

document describing representation properties, timing, 

and HTTP URL's for retrieving (sub)segments. MPD 

advertises representation properties such as bandwidth, 

codecs, resolutions, etc. 

 

The client, on its part, selects most acceptable 

alternative advertised to it, and re-evaluates its decision 

as a response to changing network conditions.  

 

More detailed introductions to DASH are provided in 

[7], [8], and [9], as well as in many other papers. 

 

 

2. ADAPTATION PARAMETERS 

 

Most representation properties provided by the MPD, 

such as resolution, aspect ratio, frame rate, etc., are 

used by the client for static adaptation – i.e., 

determining which representations can be played out 

successfully. The only parameter used for dynamic 

adaptation is bitrate. 

 

The DASH buffering model defines buffering duration, 

with the physical buffer size being a product of this 

duration and the stated representation-level bandwidth. 

This buffer is assumed to be sufficient for normal 

playback of a representation. With that said, there is no 

strict bitrate constancy requirement in DASH. 

 

While DASH segments are addressable via URL's that 

are derived directly from the MPD, finer-granularity 

addressing can be done via an index – a binary 

structure providing a mapping of playable byte ranges 

within segments to their playout times and random 

access properties. When DASH indexes are used, per-

segment bitrate information is accessible to the client, 

whereas only per-representation bandwidth is known to 

the client otherwise. Use of indexes allows for more 

optimal adaptation decisions, as well as better random 

access and trick mode functionality, and is expected 

from on-demand applications. 

 

In absence of information beyond bitrate, the client 

makes an implicit assumption that higher bitrate is 

equivalent to better perceived quality. Such greedy 

bitrate-driven rate adaptation behavior of a Microsoft 

Smooth Streaming [1] client is described in [10]. When 

indexes are used, more optimal decisions can be taken, 

 

 

 

 

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as the client has per-segment information, however this 

does not change the greedy nature of the algorithm. 

 

Greedy adaptation behavior may lead to amplified 

quality fluctuation or inefficient usage of bandwidth. 

This is due to the fact that complexity of content 

changes with time. In streaming, content is encoded 

either CBR (capped VBR, in most cases), or 

unconstrained VBR. For CBR representations, its 

bitrate is well controlled, but its quality may fluctuate 

significantly unless the bitrate is sufficiently high. 

Changing content complexity, such as switching 

between sports and static scenes in news channels 

makes it very difficult for video encoders to deliver 

consistent quality and at the same time produce 

bitstream that has a certain specified bitrate. In this 

case, bandwidth is not efficiently used. For VBR 

representation, it allows a higher bitrate to be allocated 

to the more complex scenes while fewer bits to less 

complex scenes. Its quality fluctuation is relatively 

small but far from constant. 

 

If quality information is provided, either per-

representation (as an additional attribute in the MPD), 

or per segment (in MPD, in an index, or in a separate 

resource), both quality and bitrate can be taken into 

account for the purposes. Many of the proposals to the 

MPEG Quality Driven Streaming core experiment [4] 

propose syntactical ways of providing such information. 

 

3. EVALUATION OF QUALITY BASED 

 

ADAPTATION 

 

In this section, we compare adaptation policies using 

and not using quality information. There are 4 different 

combinations on which level bitrate and/or quality are 

provided. For comparison, a typical algorithm for each 

kind policy is selected which uses the specified 

information in a simple and straightforward way. It is 

recognized that the selected algorithm may not be best 

one of its kind in performance, but it does not bias the 

comparison.  

 

3.1. Adaptation Policies 

 

1. Adaptation according to per-representation bitrate 

information (@bandwidth), denoted as RB. 

With only representation level bitrate information 

available, the only option is to match bitrate of a 

representation to the available bandwidth. At time 

when a new segment is requested, a representation 

with bitrate lower than but closest to the available 

bandwidth is selected.  This algorithm is 

equivalent to the one used in the Microsoft Smooth 

Streaming client, as described in [10]. 

 

2. Adaptation according to per-representation bitrate 

and quality information, denoted as RBRQ. 

In addition to per-representation bitrate, average 

PSNR over the whole sequence is signaled per 

 

representation to represent its quality. A quality 

threshold qth  is used in this algorithm to enhance 

RB. It works if there is enough bandwidth to 

deliver representation with quality higher than the 

threshold. Thus with quality information 

bandwidth waste is avoided. 

 

3. Adaptation according to per-segment bitrate 

information, denoted as SB. 

If the Segment Index is present in representations, 

client can download it and obtain detailed 

bitrate/size information over time. It helps to better 

match (sub)segments from representations with the 

available bandwidth to achieve a good bandwidth 

usage. This essentially is a refinement of the 

algorithm from [10] 

 

4. Adaptation according to per-segment bitrate and 

quality information, denoted as SBSQ 

It uses both bitrate and quality on (sub)segment 

level. Compared with algorithm RBRQ, the 

adaptation logic is the same but works on a finer 

granular level, segment instead of representation. 

Bandwidth adaptation and quality threshold play a 

bigger role. 

 

5. Adaptation according to per-segment bitrate and 

quality information, denoted as SBSQ 

M-SBSQ improves on the basis of SBSQ to further 

reduce quality fluctuation. When quality threshold 

works, segments of lower bitrate than possible 

under available bandwidth are downloaded, and 

the client’s buffer is then filled to a higher level. 

The higher level of buffered media data is used to 

buy time sometime later to download segments 

with higher quality than allowed by available 

bandwidth. To avoid overuse of buffered data, 

 

there is a buffer threshold - , data in buffer 

lower the threshold is only used to counter 

bandwidth fluctuation, quality improvement, i.e. 

requesting segment of higher quality than allowed 

by available bandwidth, is only performed when 

buffer level is above the threshold and should not 

result in a lower buffer level than the threshold. 

 

 

 

3.2. Experiment Setup 

 

To compare the performance of adaptation policies, 

experiment is done via simulation. Figure 1 depicts the 

experiment setup: Content Server and Client are 

connected via 1Gbps Ethernet links. For simplicity 

reason, it is assumed that available bandwidth 

measured on application level, i.e. TCP throughput is 

directly used by adaptation algorithm at client, which 

eliminates the difference resulting from bandwidth 

measurement and estimation. As long as the link 

capacity between Content Server and Client is large 

enough, traffic between the two is only restricted by 

the available bandwidth. 

 

 

 

 

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Figure 1 Experiment Setup 

 

 

3.3. Result 

 

Figure 2 depicts quality of the streamed content in 

PSNR. Though SB has the highest mean quality it 

exhibits large quality fluctuation. On quality 

fluctuation, M-SBSQ and SBSQ performs best with 

fairly small deviation. It is because M-SBSQ and 

SBSQ use quality information while SB not. RB and 

RBRQ do not perform well on both mean value and 

deviation in cases of constrained and unconstrained 

representations. The reason is that it is the maximum 

bitrate and averaged quality of all segments provided 

on representation level, which is too inaccurate to 

guide adaptation on segment basis. 

 

As for bandwidth consumption (Figure 3), SB 

consumes more bandwidth than RB. As SB always 

request segments with highest possible bitrate. For RB, 

it uses representation level bitrate which is the 

maximum bitrate of a representation (it ensures any 

segments in the representation can be delivered in time 

when available bandwidth is equal to the value). 

Apparently, some, even most segments in the 

representation have lower bitrates than this value. As a 

result, RB “overestimates” bandwidth required for 

segments in the representation and consumes a low 

bandwidth. 

 

For RBRQ and SBSQ, their performances depend on 

 

the value of the parameter quality threshold  in the 

algorithms. When the threshold is high enough it has 

no effect, RBRQ/SBSQ performs the same as RB/SB. 

When the threshold becomes smaller, it takes effect 

and segments of quality exceeding the threshold are 

replaced with those of lower quality, thus less 

bandwidth is consumed and quality of the streamed 

content becomes smoother.  

 

When =41dB, compared with SB, SBSQ saves 

consumed bandwidth about 40%. 

 

Additional quality information helps client to improve 

quality smoothness therefore quality of experience and 

at the same time reduces bandwidth consumption. 

 

Simple use of quality threshold helps to reduce quality 

fluctuation and save bandwidth, as shown in SBSQ. It 

comes with no cost of buffer level decline.  

 

Quality information can further guide quality 

improvement of streaming service, i.e. to prevent short 

term quality drop. Without quality information it is 

 

difficult if not impossible. M-SBSQ is an example 

which attempts to request segments with quality in a 

specific range, when quality of a segment exceeding an 

up bound,  it requests a segment with lower quality and 

vice versa. 

 

A comparison of results between unconstrained VBR 

and constrained VBR representation (sub figure(a) and 

sub figure (b) in Figure 4) shows constrained VBR 

benefits more from quality information and quality 

driven adaptation algorithms e.g. RBRQ, SBSQ, 

SBSQ-M, than unconstrained VBR in aspects, such as 

mean value of quality, quality deviation, bandwidth 

saving. This result can be extended to CBR, which is 

the extreme of constrained VBR with strict restriction 

on bitrate fluctuation. In a special case that available 

bandwidth remains unchanged over time and content is 

prepared in CBR, bitrate only based adaptation selects 

a single representation, however, quality driven 

adaptation performs better by selecting (sub)segments 

from different representations. The explanation is that 

from VBR to CBR, as the bitrate fluctuation reduced 

and quality fluctuation increased, quality information 

plays a more important role in adaptation. The 

conclusion is important, since in most cases of 

streaming service, content is offered as constrained 

VBR representations rather than of unconstrained ones. 

 

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N

R

 

 

 

 

 

     RB    mean 36.66 std 3.106

 

  RBRQ     mean 36.66 std 3.106

 

     SB    mean 43.47 std 3.900

 

  SBSQ     mean 40.75 std 2.238

 

M-SBSQ     mean 40.87 std 1.926

 

 

(a) unconstrained 

 

0 200 400 600 800 1000 1200 1400 1600 1800

25

 

30

 

35

 

40

 

45

 

50

 

55

 

time(s)

 

P

S

 

N

R

 

 

 

 

 

     RB    mean 38.51 std 4.183

 

  RBRQ     mean 38.36 std 4.043

 

     SB    mean 43.68 std 3.943

 

  SBSQ     mean 40.86 std 2.156

 

M-SBSQ     mean 40.95 std 1.899

 

 

(b) unconstrained 

 

Figure 2  quality of the streamed content (for SBSQ and M-

SBSQ, qth= 41dB; for M-SBSQ, delta_q=6dB) 

 

 

 

 

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0 200 400 600 800 1000 1200 1400 1600 1800

0

 

1

 

2

 

3

 

4

 

5

 

6

 

7

 

8

 

9

x 10

 

8 quality threshold 41

 

time(s)

 

d

o

 

w

n

 

lo

a

 

d

S

 

iz

e

 

(B

y

 

te

)

 

 

 

 

     RB  ( 216 MBYTE)

 

  RBRQ   ( 216 MBYTE)

 

     SB  ( 857 MBYTE)

 

  SBSQ   ( 492 MBYTE)

 

M-SBSQ   ( 516 MBYTE)

 

 

(a) unconstrained 

 

0 200 400 600 800 1000 1200 1400 1600 1800

0

 

1

 

2

 

3

 

4

 

5

 

6

 

7

 

8

 

9

 

10

x 10

 

8 quality threshold 41

 

time(s)

 

d

o

 

w

n

 

lo

a

 

d

S

 

iz

e

 

(B

y

 

te

)

 

 

 

 

     RB  ( 336 MBYTE)

 

  RBRQ   ( 312 MBYTE)

 

     SB  ( 940 MBYTE)

 

  SBSQ   ( 508 MBYTE)

 

M-SBSQ   ( 527 MBYTE)

 

 

(b) unconstrained 

 

Figure 3  bandwidth consumption (for SBSQ and M-SBSQ, 

qth= 41dB; for M-SBSQ, delta_q=6dB) 

 

 

 

0 200 400 600 800 1000 1200 1400 1600 1800

0

 

0.5

 

1

 

1.5

 

2

 

2.5

 

3

 

3.5

 

4

 

4.5

x 10

 

4

 

time(s)

 

b

u

 

ff

e

 

rs

iz

 

e

(m

 

s

)

 

 

 

 

 

     RB    min 1485 max 43826 mean 4.031150e+004

 

  RBRQ     min 1485 max 43826 mean 4.038886e+004

 

     SB    min 1165 max 43097 mean 3.745014e+004

 

  SBSQ     min 1165 max 43475 mean 3.894883e+004

 

M-SBSQ     min 1165 max 43475 mean 3.853181e+004

 

 

(a) unconstrained 

 

0 200 400 600 800 1000 1200 1400 1600 1800

0

 

0.5

 

1

 

1.5

 

2

 

2.5

 

3

 

3.5

 

4

 

4.5

x 10

 

4

 

time(s)

 

b

u

 

ff

e

 

rs

iz

 

e

(m

 

s

)

 

 

 

 

 

     RB    min 3460 max 43241 mean 4.010504e+004

 

  RBRQ     min 6097 max 43241 mean 4.013744e+004

 

     SB    min 1221 max 42255 mean 3.633247e+004

 

  SBSQ     min 1221 max 43397 mean 3.882179e+004

 

M-SBSQ     min 1221 max 43397 mean 3.844173e+004

 

 

(b) unconstrained 

 

 

Figure 4 buffer level(for SBSQ and M-SBSQ, qth= 41dB; 

for M-SBSQ, delta_q=6dB) 

 

 

 

 

 REFERENCES 

 

[1] Microsoft, IIS Smooth Streaming Technical 

Overview, Mar. 2009; http:// 

www.iis.net/learn/media/on-demand-smooth-

streaming/smooth-streaming-technical-overview. 

 

[2] R. Pantos and E.W. May, “HTTP Live Streaming”, 

IETF Internet draft, Oct. 2012 

 

[3] ISO/IEC 23009-1:2012 Information technology — 

Dynamic adaptive streaming over HTTP (DASH) 

– Part 1: Media presentation description and 

segment formats, Apr. 2012 

 

[4] MPEG, Descriptions of Core Experiments on 

DASH amendment, w13082, Shanghai, Oct. 2012. 

 

[5] C. Liu, I. Bouazizi, M. Gabbouj, “Rate Adaptation 

for Adaptive HTTP Streaming”, in Proceedings of 

the second annual ACM Conference on 

Multimedia Systems(MMSys), Feb. 2011. 

 

[6] F.Z. Yang, S. Wan, Q. Xie and H.R. Wu, “No-

reference Quality Assessment for Networked 

Video via Primary Analysis of Bit-stream,” IEEE 

Trans. CSVT, vol.20, no. 11, pp.1544-1554, Nov. 

2010. 

 

[7] I. Sodagar, “The MPEG-DASH Standard for 

Multimedia Streaming Over the Internet”, IEEE 

MultiMedia, vol.18, no.4, pp.62-67, Apr. 2011. 

 

[8] I. Sodagar and H. Pyle, Reinventing multimedia 

delivery with MPEG-DASH, Proc. SPIE 8135, 

81350R (2011) 

 

[9] M. Kar, A. Giladi, Using DASH and MPEG-2 TS 

for Adaptive Multiplatform Delivery, SCTE 

Cable-Tec Expo, November 15-17, 2011, Atlanta 

GA 

 

[10] S. Akhshabi, L. Anantakrishnan, C. Dovrolis, A. C. 

Begen, “What Happens when HTTP Adaptive 

Streaming Players Compete for Bandwidth?”, 

NOSSDAV 2012, Toronto, Canada, Jun. 2012. 

 

[11] Cisco Visual Networking Index: Global Mobile 

Data, Traffic Forecast Update, 2012–2017, 

 

[12] February 6, 2013, 

http://www.cisco.com/en/US/solutions/collateral/n

s341/ns525/ns537/ns705/ns827/white_paper_c11-

520862.html 

 

 

Shaobo Zhang  received his 

B.E.degree in Radio Engineering in 

1991, M.S. degree in Electronic and 

Communication in from Southeast 

University (Nanjing) in 1996. In the 

same year, he joined Huawei 

Technologies. He had been working 

 

in Wireless Department before 2006 where he engaged 

in development, design and planning of products for 

cellular communication. After then, he joined the 

Corporate Research Department. His research interest 

includes multimedia delivery, network optimization, 

system design and mobile communication. 

 

 

 

 

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Yangpo Xu  received B.E. degree and 

M.S. degree from Tianjin university in 

2003 and 2006 respectively. He joined 

Huawei Technologies in 2006 and 

works in the Corporate Research 

Department. His research interests are 

in video transmission, media file 

 

format and network protocols. 

 

 

 

Peiyun Di received her B.S. degree in 

Applied Mathematics and the M.S. 

degree in electronic engineering from 

Xi Dian University, Xi’An, China, in 

2004 and 2007, respectively. She is 

currently working at Huawei Tech 

 

Co.,Ltd. 

 

Alex Giladi  [M'06] is a senior 

architect at FutureWei Technologies 

(Bridgewater NJ), where he works on 

adaptive streaming ecosystem and its 

standardization. Previously he worked 

at Avail-TVN, Digital Fountain, and 

 

Harmonic Lightwaves. He is actively involved in 

 

MPEG DASH standardization since 2010, currently 

serving as the editor of two MPEG standards, Part 4 of 

MPEG DASH and Common Encryption for MPEG-2 

TS. Alex holds a MSEE degree from Stanford 

University and BSc degree from the Technion, Haifa.    

 

 

Changquan Ai received his M.S. 

degree in computer software, Xi'an, 

China. Currently He works as a 

research engineer in field of 

multimedia system. 

 

 

 

 

 

Xin Wang (xwang@huawei.com) 

has been working as a chief scientist 

in multimedia systems at Corporate 

Research of Huawei Technologies, 

USA. Prior to joining Huawei, he 

was the chief scientist at 

 

ContentGuard, Inc. He is a chair of the MPEG-M and 

MPEG-21 CEL ad hoc groups, and actively 

participates the MPEG DASH standard development. 

He is also adjunct faculty of the Department of 

Computer Science at the University of Southern 

California, Los Angeles, USA. 

 

 

 

 

 

 

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User-Adaptive Mobile Video Streaming Using MPEG-DASH 

 

Yuriy A. Reznik, InterDigital Communications, Inc. 

9710 Scranton Road, San Diego, CA 92122 

 

Email: yuriy.reznik@interdigital.com 

 

 

 

Abstract 

 

MPEG-DASH is a new international standard for 

dynamic adaptive streaming over HTTP. In this letter, 

we show how this standard can be used to design 

intelligent streaming applications adapting video 

delivery to user behavior and viewing conditions, 

resulting in better utilization of network and power 

resources.  

 

 

1  Introduction 

 

 During last two decades Internet streaming 

has experienced a dramatic growth and transformation 

from an early concept into a mainstream technology 

used for delivery of multi-media content [1-3]. A 

recently issued MPEG-DASH standard [4] consolidates 

many advances achieved in the design of streaming 

media delivery systems, including full use of the 

existing HTTP infrastructure, bandwidth adaptation 

mechanisms, latest audio and video codecs, etc. Yet, 

some challenges in implementation and deployment of 

streaming systems still exist. In particular, they arise in 

delivery of streaming video content to mobile devices, 

such as smartphones and tablets. 

 

On one hand, many mobile devices are 

already matching and surpassing HDTV sets in terms 

of graphics capabilities. They often feature high-

density “retina” screens with 720p, 1080p, and even 

higher resolutions. They also come equipped with 

powerful processors, making it possible to receive, 

decode and play HD-resolution videos. On the other 

hand, network and battery/power resources in mobile 

devices remain limited. Wireless networks, including 

latest 4G/LTE networks, are fundamentally constrained 

by capacities of their cells. Each cell’s capacity is 

shared between its users, and it can be saturated by as 

few as 105−  users simultaneously watching high-

quality videos [5]. High data rates used to transmit 

video also cause high power consumption by the 

receiving devices, draining their batteries rapidly. 

 

All these factors suggest that technologies for 

reducing bandwidth and power use in mobile video 

streaming are very much needed. In this letter we 

describe one such technology. It is based on an 

observation that in many cases, mobile phone users can 

 

see only a fraction of information projected on the 

screen. 

 

 

     

Figure  1: Characteristics of mobile viewing setup. The 

right sub-figure shows how viewing distance can be 

affected by user’s activity.   

 

 

 

Figure  2: Ambient illuminance in different 

environments [6]. 

 

 

2  Factors affecting user ability to discern 

visual content 

 

 We illustrate some factors that affect user’s 

ability to discern visual information Figures 1 and 2. 

For example, the user may hold a phone close to his 

eyes, or at arm’s length. This affects viewing angle and 

density of information seen on the screen. Ambient 

illuminance may also change significantly. The user 

may be in the office, outside under direct sunlight, in a 

shadow, or in a completely dark area. Reflection of 

ambient light from the screen lowers the contrast of 

video or images seen by the user [6]. Finally, the user 

may pay full attention to visual content on the screen, 

or he could be distracted. 

 

Together with characteristics of the mobile 

display and user vision, all these factors affect the 

capacity of the “visual channel”, serving as the last link 

in a communication system delivering information to 

the user. The main idea of this letter, as well as several 

of our related publications [7, 8] is to show that 

 

 

 

 

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characteristics of this last link can also be effectively 

measured and utilized in optimizing streaming video 

delivery. The recently developed MPEG-DASH 

standard offers an excellent framework using which 

this idea can be realized. 

 

 

 

 

Figure 3: Illustration of functionality of mobile DASH-

based streaming system. The multimedia content is 

encoded at encoded at multiple rates, and segmented in 

chunks allowing client to select portions that can be 

delivered in real-time, while also adopting to changing 

network bandwidth.  

 

   

3 MPEG DASH-standard: the basics 

 

 We present a conceptual model of DASH-

based mobile video streaming system in Figure 2. The 

original video content is captured, encoded, and placed 

on an HTTP server. To scale distribution, the content 

may also be pushed to many servers forming a Content 

Distribution Network (CDN). It is typically the web 

browser or a streaming client application running on a 

mobile phone (UE) that discovers this content, 

retrieves it, and shows it on a mobile device. 

3.1  Content preparation 

 

In order to support bandwidth adaptive 

streaming, the content is usually encoded at a plurality 

of bit rates. Such encodings are also prepared such that 

they consist of multiple segments with time-aligned 

boundaries, allowing switches between encodings at 

different rates. In MPEG DASH standard, points at 

which switching is allowed are called stream access 

points (SAP). In the simplest case, SAP may 

correspond to an I- or IDR- video frame, allowing 

sequential decoding of all frames that follow. In 

addition to producing encoded media streams the 

encoder also produces a file containing information 

about parameters of each of the encodings and URL 

links to them. This file is called media presentation 

description (.mpd) file. 

 

3.2  Adaptation to bandwidth changes 

 The streaming session is controlled entirely 

 

by the DASH streaming client. It opens an HTTP 

connection to the server, retrieves the .mpd file, and 

learns about different encodings (representations) that 

are available on the server. Then it picks representation 

with most suitable bitrate, and start retrieving its 

segments by issuing HTTP GET requests. As 

bandwidth changes, the streaming client may request 

 

segments encoded at different bit rates, allowing 

uninterrupted playback of the content. We illustrate 

this in Figure 3. 

3.3 Communication of encoding 

parameters 

 

 MPEG-DASH media presentation description 

file allows encoders to share specific parameters of 

each encoded version of the content. In case of video, 

these parameters include resolution (width ×  height), 

pixel aspect ratio, frame rate, and required bandwidth. 

When the content is prepared, the encoder may choose 

to use different combination of these parameters to 

produce encodings for each target bitrate. The encoder 

may also produce multiple encodings considering 

different screen resolutions and other specific 

capabilities of target devices, allowing streaming 

clients to pick versions that are optimized for each 

particular device. 

 

 

 

 Figure  4: Illustration of functionality of DASH 

streaming client incorporating adaptation to user 

behavior and viewing conditions. 

 

 

4  Enabling adaptation to user behavior 

and viewing conditions 

 

We provide conceptual illustration of user-

adaptive design of DASH streaming client in Figure 3. 

In order to adapt to viewing conditions, the client uses 

sensors of a mobile device, such as front-facing camera 

and accelerometer to detect the presence of the user, 

his proximity, pose, and viewing angle. The client also 

uses ambient illuminance sensor and information about 

brightness settings of the screen to estimate effective 

contrast ration of the screen. 

 

Using these estimates, the client obtains 

minimum characteristics of encoded video, such as 

spatial resolution, framerate, and bitrate that are 

sufficient to achieve high level of visual quality. In 

finding such characteristics the client can use spatio-

temporal contrast sensitivity functions [9] or other 

related results from studies on human vision and video 

coding. Once such characteristics are obtained, the 

client searches through a list of available video 

representations and selects one that is best suited for 

delivery. 

 

As illustrated in Figure 4, adaptation to 

viewing distance and contrast can result in lowering 

bandwidth required to receive video. Increase in 

viewing distance lowers our ability to discern 

individual pixels and hence it becomes possible to 

 

 

 

 

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select representations encoded using lower resolution 

and bitrate. Likewise increase of ambient illuminance 

lowers effective contrast of the screen and range of 

spatial frequencies that we can see. This also opens 

opportunity for lowering the resolution and required 

bitrate. For additional details the reader is referred to 

our related publications [7, 8]. 

 

In cases when client detects that user is not 

present next to the device, even more significant 

bandwidth savings are possible. For example, the client 

may stop receiving video while continuing playing 

only audio track. 

 

As bandwidth usage is directly related to 

power consumption in mobile phones, the above 

described optimizations can also result in increased 

battery life. 

5  Conclusion 

 

 In this letter we have shown that MPEG-

DASH standard enables design of intelligent streaming 

systems adapting not only to bandwidth but also to 

factors affecting user ability to see visual information. 

Such adaptation can result in reduced bandwidth usage, 

increased battery life, and improved quality of user 

experience. 

 

 

 

  

Figure  5: Example of rate allocation achieving 

approximately the same level of perceived quality 

under different viewing distances and contrast rates. 

This particular allocation was obtained assuming 

reproduction on a mobile device with 720 p-resolution 

screen, 340  dpi pixel density, and when using the 

H.264 (Main profile) video encoder. 

 

   

References  

[1]  D. Wu, Y.T. Hou, W. Zhu, Y-Q. Zhang, and J.M. 

Peha, “Streaming video over the Internet: approaches 

 

and directions,”  IEEE Trans. Cir. Syst. Video Tech., 

Mar 2001, vol. 11, no. 3, pp. 282-300. 

[2]  G. J. Conklin, G. S. Greenbaum, K. O. Lillevold, A. 

F. Lippman, and Y. A. Reznik, “Video Coding for 

Streaming Media Delivery on the Internet,”  IEEE 

Trans. Cir. Syst. Video Tech., Mar 2001, vol. 11, no. 3, 

pp. 20-34. 

[3]  I. Sodagar, “The MPEG-DASH Standard for 

Multimedia Streaming Over the Internet,”  IEEE 

Multimedia, Oct-Nov, 2011. 

[4]  ISO/IEC 23009-1  Information Technology – 

Dynamic adaptive streaming over HTTP (DASH) – 

Part 1: Media presentation description and segment 

formats, ISO/IEC, January 5, 2012. 

[5]  A. Talukdar, M. Cudak, and A. Ghosh, “Streaming 

Video Capacities of LTE Air Interface,”  Proc. IEEE 

Int. Conf. Comm. (ICC), 2010, pp. 1-5. 

[6]  J. Bergquist, “Resolution and contrast requirements 

on mobile displays for different applications in varying 

luminous environments,”  Proc. 2nd Int. Symp. 

Nanovision Science., 2005, pp. 143-145. 

[7]  Y. Reznik, et al., “User-adaptive mobile video 

streaming,”  Proc. of IEEE Visual Communication and 

Image Processing, Aug 2012. 

[8]  R.Vanam, Y.Reznik, “Improving the Efficiency of 

Video Coding by using Perceptual Preprocessing 

Filter,”  Proc. Data Compression Conference, March 

2013. 

[9]  D. H. Kelly, “Motion and vision. II Stabilized 

spatio-temporal threshold surface,”  Journal of the 

Optical Society of America, 1969, vol. 69, pp. 1340-

1349. 

 

 

 

 

Yuriy A. Reznik (M’97-

SM’07) is a Director at 

InterDigital Communications 

(San Diego, CA), where he 

leads R&D in multimedia 

coding and delivery over 

wireless networks. He also 

serves as an editor of Part 3 of 

MPEG-DASH standard in 

 

MPEG. Previously, he worked at Qualcomm (2005-

20011), and RealNetworks (1998-2005), and also 

stayed as Visiting Scholar at Stanford University 

(2008). He holds a Ph.D. degree in Computer Science 

from Kiev University. He has authored/co-authored 

over 80 conference and journal papers, and co-invented 

14 issued (and over 50 pending) US patents. 

 

 

 

 

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CALL FOR PAPERS 

 

COMMUNICATION SOFTWARE, SERVICES, AND MULTIMEDIA 

 

APPLICATION SYMPOSIUM 

 

 

Symposium Co-Chairs 

Vincent Wong, University of British Columbia, Canada 

vincentw@ece.ubc.ca 

Liang Zhou, Nanjing University of Posts and Telecommunications, China 

liang.zhou@ieee.org 

 

 

 

Scope and Topics of Interest 

The Communications Software, Services and Multimedia Application Symposium will provide an international 

technical forum for discussing and presenting recent research results on any aspects of software, services, and 

multimedia communications. It aims at bringing together experts from industry and academia to exchange ideas 

and present results on advancing the state-of-the-art and overcoming research on the challenging issues related 

to the software design, system deployment of services, and multimedia applications over heterogeneous 

networks. Papers may present theories, techniques, applications, or practical experiences related to that. Topics 

of interest for this Symposium include, but are not limited to: 

 

 

Multimedia Applications and Services 

 

Multimedia delivery and streaming over wired and wireless networks 

Cross-layer optimization for multimedia service support 

Multicast, broadcast and IPTV 

Multimedia computing systems and human-machine interaction 

Interactive media and immersive environments 

Multimedia content analysis and search 

Multimedia databases and digital libraries 

Converged application/communication servers and services 

Multimedia security and privacy 

Multimedia analysis and social media 

 

 

Network and Service Management and Provisioning 

 

Multimedia QoS provisioning 

Multimedia streaming over mobile social networks and service overlay networks 

Service creation, delivery, management 

Virtual home environment and network management 

Charging, pricing, business models 

Security and privacy in network and service management 

Cooperative networking for streaming media content 

 

 

Next Generation Services and Service Platforms 

 

Location-based services 

Social networking communication services 

Mobile services and service platforms 

 

 

 

 

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Home network service platforms 

VoP2P and P2P-SIP services 

 

 

Software and Protocol Technologies for Advanced Service Support 

 

Ubiquitous computing services and applications 

Networked autonomous systems 

Communications software in vehicular communications 

Web services and distributed software technology 

Software for distributed systems and applications, including smart grid and cloud computing services 

Peer-to-Peer technologies for communication services 

Context awareness and personalization 

 

 

 

Submission Guidelines 

Prospective authors are invited to submit original technical papers by the deadline of 15 March 2013 for 

publication in the IEEE Globecom 2013 Conference Proceedings and for presentation at the conference. 

Submissions will be accepted through EDAS. All submissions must be written in English and be at most six (6) 

printed pages in length, including figures. For full details, please visit the following website: 

 

 

http://www.ieee-globecom.org/2013/submguide.html 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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MMTC OFFICERS 

 

 

 

 

CHAIR                                                             STEERING COMMITTEE CHAIR 

 

 

Jianwei Huang                   Pascal Frossard      

The Chinese University of Hong Kong            EPFL, Switzerland 

China  

 

 

 

VICE CHAIRS 

 

 

Kai Yang                                                           Chonggang Wang     

Bell Labs, Alcatel-Lucent                                 InterDigital Communications 

USA       USA 

 

Yonggang Wen                                                 Luigi Atzori 

Nanyang Technological University                  University of Cagliari 

Singapore                    Italy     

 

 

SECRETARY 

 

 

Liang Zhou  

Nanjing University of Posts and Telecommunications  

China 

 

    

 

 

 

E-LETTER BOARD MEMBERS 

 

 

 

Shiwen Mao Director Aburn University USA 

 

Guosen Yue Co-Director NEC labs USA 

 

Periklis Chatzimisios Co-Director Alexander Technological Educational Institute of 

 

Thessaloniki 

 

Greece 

 

Florin Ciucu Editor TU Berlin Germany 

 

Markus Fiedler Editor Blekinge Institute of Technology Sweden 

 

Michelle X. Gong Editor Intel Labs USA 

 

Cheng-Hsin Hsu Editor National Tsing Hua University Taiwan 

 

Zhu Liu Editor AT&T USA 

 

Konstantinos Samdanis Editor NEC Labs Germany 

 

Joerg Widmer Editor Institute IMDEA Networks Spain 

 

Yik Chung Wu Editor The University of Hong Kong Hong Kong 

 

Yan Zhang Editor Simula Research Laboratory Norway 

 

 

 

pages 39-41.pdf

Message from MMTC Chair

EMERGING TOPICS: SPECIAL ISSUE ON

QoE Aware Optimization in Mobile Networks

Guest Editors: Tasos Dagiuklas, TEI of Mesolonghi, Greece, Weisi Lin, Nanyang Technological University, Singapore, Adlen Ksentini, University of Rennes 1, France

 

A QoE cross layer approach to model media experiences

Andrew Perkis

NTNU, Norway

andrew@iet.ntnu.no

 

Context Aware Quality of Experience for Audio-Visual Service Groups

M. Tourad Diallo1, H. Moustafa2, H.Afifi3, N. Marechal1

(1) Orange Labs, France, (2) Intel Labs USA, (3) Institut Mines-Télécom France

{mamadoutourad.diallo, nicolas.marechal}@orange.com, hassnaa.moustafa@intel.com, hossam.afifi@mines-telecom.fr

 

No-reference IPTV Video Quality Assessment Based on End-to-End Visual Distortion Estimation

Ning Liao and Zhibo Chen

Technicolor Research & Innovation, Beijing, China

ning.liao@technicolor.com, chenzb@ieee.org

 

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Video Quality as a Driver for Traffic Management with Multiple Subscriber Classes

Martín Varela and Janne Seppänen

VTT Technical Research Centre of Finland

{martin.varela, janne.seppanen}@vtt.fi

 

Cross-layer Design for Quality-Driven Multi-user Multimedia

Transmission in Mobile Networks

Maria G. Martini

Kingston University London

{m.martini}@kingston.ac.uk

 

INDUSTRIAL COLUMN: SPEICAL ISSUE ON

“DYNAMIC ADAPTIVE STREAMING OVER HTTP”

Guest Editors: Alex Giladi, Zhenyu Wu, Futurewei Technologies, U.S.A

and Guosen Yue, NEC Laboratories America

 

MPEG DASH: A Brief Introduction

Alex Giladi, FutureWei Technologies  400 Crossing Blvd., Bridgewater NJ, 08807 Email: alex.giladi@huawei.com

 

Optimizing DASH Delivery Services over Wireless Networks

Ozgur Oyman, Intel Labs, Santa Clara, CA 95054 USA

ozgur.oyman@intel.com

 

Fair Share Dynamic Adaptive Streaming over HTTP

Christopher Mueller, Stefan Lederer, and Christian Timmerer

Multimedia Communication (MMC) Research Group, Institute of Information Technology (ITEC)

Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

{Christopher.Mueller, Stefan.Lederer, Christian.Timmerer}@itec.aau.at

Abstract

 

 

Quality Driven Streaming Using MPEG-DASH

Shaobo Zhang, Yangpo Xu, Peiyun Di, Alex Giladi, Changquan Ai, Xin Wang

Huawei Technologies

Abstract

 

 

User-Adaptive Mobile Video Streaming Using MPEG-DASH

Yuriy A. Reznik, InterDigital Communications, Inc. 9710 Scranton Road, San Diego, CA 92122 Email: yuriy.reznik@interdigital.com

 

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http://www.ieee-globecom.org/2013/submguide.html

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