The Vault

Video Aware Logical Channel Based Scheduling For LTE Systems
Research Paper / Feb 2014

VIDEO AWARE LOGICAL CHANNEL BASED SCHEDULING FOR LTE SYSTEMS A. Balasubramanian?, L. Ma?, A. Rapaport†, W. Liu†, G. Sternberg†, and A. Zeira? ? InterDigital Communications, Inc., San Diego, CA 92121, USA †InterDigital Communications, Inc., King of Prussia, PA 19406, USA ABSTRACT This paper considers a downlink LTE system where a bases- tation (eNodeB) is serving video traffic to many users in a cell. The video traffic is separated into multiple streams (log- ical channels) based on the importance or priority of video packets. The objective is to maximize video quality by ser- vicing appropriate video streams and users while taking into account the resource constrained wireless channel. It is shown that one can obtain significant gains in video quality by de- termining the quality of service (QoS) and fairness param- eters on a per-stream basis, rather than in ‘user-based’ ap- proach where all the streams are lumped together. Motivated by video conferencing applications, we provide simulation results for the case when video traffic is separated into log- ical channels based on a Hierarchical-P encoding structure. Furthermore, we demonstrate the gains in video quality that can be obtained by scheduling across logical channels. With the exploding growth in mobile traffic expected to happen in 4G systems, this study suggests potential benefits of a logical channel based approach as compared to conventional user- based schemes. Index Terms— Video aware scheduling, QoS, Propor- tional Fair, LTE, Hierarchical-P. 1. INTRODUCTION There has been a rapid growth in mobile multimedia traffic recently due to various reasons such as introduction of smart phones like the iPhone, and iPad among others. These de- vices are endowed with advanced multimedia capabilities like video streaming, high resolution display and the ability to support interactive applications like video conferencing and video chatting. The increased availability of advanced mobile multimedia devices is matched by a plethora of video enabled applications that are available in the market such as Facetime which enables video display along with conventional voice calls. This explosion in multimedia traffic is likely to con- tinue and Cisco [1] has predicted that mobile traffic will be dominated by video which will exceed 90% of the global con- sumer traffic in a few years [2]. Although 4G systems (such as LTE, LTE-A) can deliver higher data rates than their coun- terpart 3G/2G systems, the rate of video traffic explosion as predicted by Cisco [1], could easily outgrow the increased capacity offered by such systems. Moreover realtime video communications over mobile networks are constrained due to the strict latency requirements imposed by such applications. It is typical to add redundancy into video bitstreams to com- bat packet losses in the network. The fact that the video pack- ets are not equally important due to different levels of redun- dancy, presents an opportunity for the network to differentiate video packets intelligently to optimize video quality. We briefly review related work. In [3], authors propose scheduling policies for LTE systems, but do not consider video traffic. Liebl et al., [4] have proposed a scheduling scheme where, by knowing the future channel behavior, transmission of video packets to users with favorable channel conditions is performed until a deadline approaches. The problem with this approach is that, it may not always be possible to precisely know the future channel state (for exam- ple, if the users have high mobility). A content aware utility function is maximized as the scheduling performance metric in [5] on a per-user basis whereas in this work, we consider resource allocation on a per-stream basis for all users. In [6], stream-based scheduling is performed, however the au- thors have considered a Multimedia Broadcast and Multicast (MBMS) scenario in LTE, whereas the proposed scheme in this paper is for non-broadcast, non-multicast scenarios. Our main contribution is to propose a logical channel based (stream-based) scheduling policy that considers a stream in the order of priority, and services users taking into account multiuser diversity, fairness, and QoS metrics. It is important to emphasize that the fairness and QoS metrics are considered on a per-stream basis rather than by lumping all the streams as one entity. Furthermore, wireless resources are allocated to lower priority streams only after allocating just enough resources for higher priority streams. The remainder of this paper is organized as follows. Sec- tion 2 contains the video and wireless system model. In Sec- tion 3 we propose a logical channel based scheduling policy. Section 4 discusses the simulation results. Finally, Section 5 concludes the paper. 2. SYSTEMMODEL Motivated by real time video applications, we consider an end-to-end system such as video tele-conferencing (for ex- ample, supported by the RTP/UDP protocol). We assume that the losses between the packet gateway and eNodeB are negli- gible (which is typically the case). Therefore, the focus of this paper is on the link between eNodeB and UE air interface. 2.1. Video System Model It is assumed that video is encoded according to a hierarchical- P structure. Fig.1 shows three layers of hierarchy (numbered 1 through 3) where layer-2 frames are predicted from layer-1, while layer-3 are predicted from layer-1 and layer-2. The impact of losing a layer-1 frame is quite large, because losing such a frame affects all subsequent frames across all lay- ers. A layer-2 frame is less important than a layer-1 frame because the impact of losing a layer-2 frame affects only a layer-3 frame. Similarly, layer-3 frames are the least impor- tant. As such, it makes sense to prioritize video frames in accordance with the impact of their loss. This leads to layer-1 frames being higher priority than layer-2 frames, etc. This encoding structure provides flexibility in adapting to network conditions aside from its error resilience properties and its attractiveness for video conferencing applications. For ex- ample, layer-3 frames can be dropped thus halving the frame rate (and the bit rate) without significantly affecting the video quality, as layer-3 frames are not used for prediction. More details on the properties of the hierarchical-P structure can be found in [7]. I (1) P (3) P (3) P (2) P (1) P (3) P (3) P (2) P (1) P (3) P (3) P (2) P (1) Fig. 1: Hierarchical-P video encoding structure The encoded video bitstream can be separated into differ- ent streams by assigning distinct port numbers to each layer of the video coding hierachy (shown in Fig.1), thereby creating several flows of video bitstreams based on their importance. Fig.2 shows an example where encoded video is separated into, L = 3 flows (logical channels) as it enters each user’s transmit buffer. The eNodeB chooses packets from logical channels and delivers them to the appropriate users through an error prone wireless channel. Packets that are lost due to the wireless channel or due to buffer overflow at the eN- odeB are concealed using simple error concealment scheme, namely, frame copy for decoding the video. 2.2. Wireless Model We consider an LTE cellular network with an eNodeB serv- ing N active wireless users with a (4x2) antenna configura- tion. There is a transmit buffer for every user at the eNodeB that contains L logical channels which stores video packets of appropriate layers as explained in Section 2.1. The system bandwidth is divided intom physical resource blocks (PRBs) which are further divided intoM subbands with (M−1) sub- bands each having p = dmM e consecutive PRBs, and one sub- band having (m− (M − 1)p) consecutive PRBs [8]. The eN- odeB can allocateM subbands toN users in the system every transmission time interval (TTI) of 1ms. Each subband can be allocated to one or more logical channels to at most one user every TTI. We do not assume an infinitely backlogged trans- mit buffer model in this paper (that is, there are time instants during which there may not be any data to transmit). It is im- portant to note that the presence of multiple logical channels in each user’s transmit buffer gives additional freedom to al- locate subbands based on fairness, and the buffer occupancy of each logical channel as opposed to lumping all the logical channels together and considering the resultant buffer as one entity. eNodeB (Scheduler) Logical Channel-1 Logical Channel-L User-1 . . . . . . Logical Channel-1 Logical Channel-L User-N UE-1 UE-N . . . . Fig. 2: Wireless System Model For every subband, a user-dependent channel quality indi- cator (CQI) that is indicative of channel conditions is assumed to be known at the eNodeB in addition to user-dependent rank and precoding matrix. Furthermore, the eNodeB always chooses spatial multiplexing mode whenever the rank of the channel is greater than one. 3. PROPOSED SCHEDULER Similar to the approach in [3], we can define an objective function accounting for the importance of packets belonging to different logical channels, and formulate the problem of optimizing the video quality as a integer programming prob- lem. Our formulation will include the one in [3] as a special case. As the problem in [3] is NP-complete, so would be our formulation. Therefore, instead of trying to find a practical algorithm for the problem of optimizing a pre-defined objec- tive function with various constraints, we design an algorithm based on the characteristics of video traffic and then show that the algorithm does reasonably well in achieving the goal of enhancing the video quality. Fig.3 shows the average peak signal-to-noise ratio (PSNR) loss of a decoded video sequence due to a packet loss from each of the logical channels for several Quantization Param- eters (QPs). It is clear that the PSNR loss due to packet loss from logical channel-1 is much higher than that of other logical channels. From the error propagation characteristic of different layers of video frames discussed in [7], and the PSNR loss characteristic depicted in Fig.3, one of the possi- bilities would be to look at a strict priority based scheduling scheme where resources (subbands) are allocated so as to meet the requirements for logical channel-1, and use the remaining resources (if available) for lower priority logical channels. Another advantage in employing this approach is that it enables us to compute the fairness, and QoS metrics at the logical channel level which provides finer granular- ity, instead of having to compute these metrics coarsely by lumping all the logical channels together and treating them as one entity. We show that this simple scheme indeed gives us significant gains when compared with the state-of-the-art scheduling schemes. 1 2 3 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Hierarchical P temporal layer Av er ag e PS NR L os s (dB ) QP=30 QP=27 QP=24 Fig. 3: Average PSNR loss due to a packet loss in different logical channels For subband c, let rci represent the transmit data size for user i that can be obtained from the user-dependent CQI re- ported for this subband. Let Rci,j represent the number of bits transmitted from the jth logical channel of user-i in subband c. Let R˜i,j(t) denote user-i’s exponentially weighted moving average throughput from logical channel-j achieved until TTI t, which is calculated as: R˜i,j(t) = (1− α)R˜i,j(t− 1) + α ∑ c R˜ci,j(t− 1) (1) where R˜ci,j denotes the data successfully delivered in subband c from the jth logical channel of ith user’s buffer, and α de- notes the averaging constant. The proposed scheme considers a logical channel-j (in the order of decreasing priority) for all the users in the system at a given TTI and allocates a subband c to user-k that satisfies the following: k = argmax i Pi,j Qi,j r c i (2) Here Pi,j represents the proportional fairness (PF) weight for logical channel-j of user-i, which is calculated as Pi,j = 1/R˜i,j , where R˜i,j is computed according to (1) (t omitted for simplicity), Qi,j represents the head of line (HOL) delay for logical channel-j of user-i. For ease of notation, we do not explicitly show the dependence of k on j, c in (2). Since we do not assume an infinitely backlogged transmit buffer in this paper, it is possible that for user-k, logical channel-j may not have any packets to transmit, while logical channel-(j + 1) might have some, in which case subband c would not be allo- cated to user-k, but instead to the next best user that satisfies (2). The detailed algorithm is outlined below. Algorithm Logical Channel Scheduling 1: Let S = {1, 2 . . .M} denote the subbands in the system. 2: Let Bi,j denote the amount of data waiting to be trans- mitted in logical channel-j of user-i from eNodeB. 3: ∀ i, j: Ei,j = 0 denotes the estimated amount of data serviced from logical channel-j of user-i. 4: for logical channel, j = 1, 2, . . . L do 5: Let U = {1, 2 . . . N} denote the users in the system. 6: for every subband c ∈ S do 7: select the best user, k ∈ U according to equation (2). 8: if Bk,j ≤ Ek,j then 9: // Do not consider this user for this subband 10: U ← U \ {k}. 11: rck = 0. Goto step (7). 12: else 13: Assign subband c to user k chosen in step (7). 14: S ← S \ {c} 15: end if 16: Update Ek,j ← Ek,j + rck 17: end for 18: end for 4. SIMULATION RESULTS AND DISCUSSION Simulations were performed on a system-level simulator for the LTE air-interface with abstractions of the application, transport, medium access control, and the physical layers built on the MATLAB platform. Some of the important system parameters that were used in the simulation are as follows [8]: bandwidth:10MHz, number of physical resource blocks:50, number of subbands:7 with 38 wireless users in the cell randomly distributed and α = 0.0029 (refer (1)). The eNodeB sends video traffic encoded into three layers us- ing Hierarchical-P [9] described in Section 2.1 to each user. Each user decodes the frames that are received in error using frame-copy based error concealment (where packets that are received in error are copied from the previous frame). The PSNR of the decoded frames was chosen to be the perfor- mance metric for evaluating different scheduling algorithms. Furthermore we assume that a packet is lost if it is not trans- mitted within its delay constraint (in practical LTE systems, the delay limit for the eNodeB to UE link is normally kept below 50ms). We have compared the state-of-art algorithms like propor- tional fair (PF) [10], Maximum Largest Weighted Delay First scheme (MLWDF) (wherein we lump all the logical channels together) [11], with the proposed logical channel (LC) based scheme. As we have multiple logical channels correspond- ing to a user in our system, we have also considered a vari- ant of MLWDF which takes into account the logical channels (which we henceforth call MLWDFLC) wherein the HOL de- lay of a user is taken to be the maximum of the HOL delay of all its logical channels. This scheme ensures that users that have any of their logical channels backlogged (as compared to others) are given more priority. More specifically, in ML- WDFLC, for a subband c, we choose user-k that satisfies: k = argmax i Pi ( argmax j Qi,j ) rci where Pi is the PF weight of user-i, calculated as Pi = 1/R˜i, where R˜i denotes the exponentially weighted moving average throughput obtained by lumping all the logical channels, and Qi,j , r c i are as described above. 10 15 20 25 30 35 40 45 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Frame PSNR (dB) CD F PF MLWDF MLWDFLC LC Fig. 4: Cumulative distribution of the decoded frame PSNR for Soccer sequence. Fig.4 and Fig.5 depict the cumulative distribution of the decoded PSNR of all frames of all the users in the system for various schemes for the soccer (delay constraint of 50ms) and foreman sequences (delay constraint of 30ms) respectively. It is clear that the logical channel approach gives significant and consistent gains over the other algorithms. To see why this should be case, Fig.6 shows the packet loss rate due to congestion (that is, the percentage of packets dropped at the transmitter due to violation of the delay constraint) in logical 10 15 20 25 30 35 40 45 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Frame PSNR (dB) CD F PF MLWDF MLWDFLC LC Fig. 5: Cumulative distribution of the decoded frame PSNR for Foreman sequence Foreman Soccer 0 5 10 15 Pa ck et L os s R at e (% ) MLWDFLC LC Fig. 6: Logical Channel-1 Rate of Packet Loss Due to Con- gestion channel-1 for different sequences. It is clear that the LC ap- proach has far less packet loss than the MLWDFLC (conges- tion losses for individual logical channels are not applicable for PF and MLWDF as all the logical channels are lumped together). However, it should be noted that the packet loss rate of other logical channels are higher for the LC scheme than MLWDFLC (not shown for lack of space). This is due to the fact that LC approach uses the importance of the video packets in allocating resources (subbands) to users (and to the logical channels) exhibiting this phenomenon which proves beneficial in improving the video quality. It is instructive to see that the gains in soccer sequence is higher than that of foreman. For example, in the case of soccer sequence there is a 12dB gain for the median user between LC and MLWDF, while there is none for the foreman sequence. This is because there is less dependency among the frames in soccer sequence (due to high motion) as compared to foreman, making the er- ror concealment less effective for soccer than foreman. 5. CONCLUSION We have considered a logical channel based scheduling scheme in which fairness and QoS metrics are considered on a logical channel basis to service users, and this scheme has been shown to perform better than the state-of-the-art scheduling schemes. 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