RATE CONTROL AND STREAM ADAPTATION FOR SCALABLE VIDEO STREAMING OVER MULTIPLE ACCESS NETWORKS. Cheng-Hsin Hsu, Nikolaos M. Freris, Jatinder Pal Singh

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1 RATE CONTROL AND STREAM ADAPTATION FOR SCALABLE VIDEO STREAMING OVER MULTIPLE ACCESS NETWORKS Cheng-Hsin Hsu, Nikolaos M. Freris, Jatinder Pal Singh Deutsche Telekom R&D Laboratories USA El Camino Real 221 Los Altos, CA 9422 Xiaoqing Zhu Cisco Systems, Inc. 42 East Tasman Drive San Jose, CA 9134 ABSTRACT In a multihomed video streaming system, a video sequence is simultaneously transmitted over multiple access networks to a client. In this paper, we formulate the rate control and stream adaptation problems into a unified optimization problem, which determines the sending rates of individual networks, selects which video packets to transmit, and assigns each packet to an access network. We propose two heuristic algorithms with a trade-off between optimality and computational complexity. One of the proposed algorithms runs faster, while the other one results in better video quality. We propose a hybrid algorithm that demonstrates a good balance between optimality and computational complexity. We conduct extensive packet-level simulations to evaluate our algorithms using real network conditions and actual scalable video streams. We compare our algorithms against the rate control algorithms defined in the Datagram Congestion Control Protocol (DCCP) standard. The simulation results show that our algorithms significantly outperform current systems while being TCP-friendly. Our algorithms achieve at least 1 db quality improvement over DCCP and result in up to 83% packet delivery delay reduction. Index Terms Scalable video streaming, distortion minimization, rate allocation, stream adaptation 1. INTRODUCTION Increasing number of fixed and mobile Internet devices have access to multiple networks. Multihomed systems [1, 2] concurrently utilize multiple access networks for higher aggregate bandwidth, better load balancing, more pervasive connectivity, improved error resilience, and lower network latency [3]. These benefits are crucial for both end users and service providers. In particular, end users who exclusively use 3G data networks may suffer from insufficient network capacity [4], whereas those who only connect to wireless local-area networks (WLANs) may suffer from frequent disconnections as each access point only covers a small area. Furthermore, a service provider may save its transit cost by offloading Internet traffic to other access networks. In fact, several US mobile service providers recently report a staggering -fold increase in data traffic due to smartphone users [, 6]. Since increasingly more cellular data plans are becoming flat-rate, these mobile service providers need to offload the Internet traffic over WLANs to remain profitable. Video streaming has high bandwidth and stringent delay requirements, and can greatly benefit from multihoming. In a multihomed streaming system, the server needs to carefully choose the streaming rates: choosing a low rate may result in under-utilization of the access network, while selecting a high rate may lead to network congestion and video packets missing their playout deadlines. Hence, effective rate control for a good trade-off between the achieved throughput and experienced delay is important. Once the streaming rates are determined, the server needs to convert the video stream into a format that can be delivered to the client on time. We refer to this conversion as stream adaptation, which is traditionally implemented via computationally intensive transcoding [7] which does not scale well. In contrast, scalable video coding [8] supports flexible stream adaptation, and further enables service providers to save the cost of streaming servers and transcoders. Although scalable video coding comes with a small coding inefficiency, the recent H.264/SVC standard is reported to be quite efficient [8]; it even outperforms some nonscalable coders, such as MPEG-4 Advanced Simple Profile (ASP) [9]. We study scalable video streaming and consider the problem of determining the portions of a scalable video stream that should be sent over individual access networks under several constraints including available bit rate (ABR) and round-trip time (RTT) of each access network, and rate-distortion (R-D) functions of individual video frames. Our contributions can be summarized as follows: We formulate the rate control and stream adaptation problems over multiple access networks into a unified optimization problem. Solving this problem jointly: (i) determines sending rates of individual access networks, (ii) selects video packets to transmit, and (iii) assigns each packet to an access network, in order to maximize video quality at a client. 1

2 We propose two heuristic algorithms to solve our problem, which provide a trade-off between optimality and computational complexity. We present a hybrid algorithm for a good balance of this trade-off. We conduct extensive trace-driven, packet-level simulations using actual network conditions and real scalable streams. Our simulation results show that our proposed algorithms outperform the rate control algorithms defined in the Datagram Congestion Control Protocol (DCCP) standard [1]. The rest of this paper is organized as follows. In Sec. 2, we present related work. We present the problem formulation in Sec. 3. In Sec. 4, we propose and analyze three heuristic algorithms. The proposed algorithms are evaluated in Sec.. Sec. 6 concludes the paper. 2. RELATED WORK Rate control of nonscalable video streams for multihomed clients has been investigated in [1, 2, 11]: Singh et al. [11] propose a solution based on stochastic control of Markov Decision Processes, Alpcan et al. [2] give a solution based on H -optimal control of linear dynamic systems, and Zhu et al. [1] present a solution based on convex optimization. Efficient stream adaptation using scalable streams has also been studied [12 1]: Hefeeda and Hsu [12] consider the stream adaptation problem of a Fine-Grained Scalable (FGS) stream between one receiver and multiple senders. Amonou et al. [13] study the problem of prioritizing video packets for H.264/SVC streams. They empirically calculate the distortion impact of dropping each video packet, and assign higher priority to video packets with higher impact values. Mansour et al. [14] study the stream adaptation problem in a single-hop wireless network, where the receivers share a given network capacity for receiving FGS streams from the base station. Sun et al. [1] propose an R-D model for FGS streams coded by H.264/SVC, which is based on a generalized Gaussian distribution source model. To the best of our knowledge, our work is the first that simultaneously considers the end-to-end rate control and scalable stream adaptation for multihomed clients. Previous works either consider nonscalable video streaming [1, 2, 11], or concentrate on scalable stream adaptation without accounting for diverse and dynamic network conditions [12 1]. 3. PROBLEM FORMULATION 3.1. Heterogeneous Access Networks We consider the multihomed video streaming problem, in which a streaming server sends video data over N heterogeneous and time-varying access networks to a client 1. Fig. 1 1 Scalable video streaming over a single access network is a special case of our analysis with N = 1. Server Internet r 1 r 2 r 3 WiFi Ethernet 3G Cellular Client Fig. 1. Multihomed scalable video streaming. GoP Fig. 2. SVC prediction structure. shows an example forn = 3. The network condition of each access network n (1 n N) is described by its available bit rate (ABR) c n and round-trip time (RTT) τ n, which are periodically measured using a lightweight measurement tool, such as Abing [16]. Based on network conditions, the server splits the video stream into N transport streams, and transmits transport stream n over access network n. We let r n be the transport stream rate over access network n, and r := N n=1 r n be the total video streaming rate. Operating a network at a rate r n close to its available bit rate c n leads to higher delays and packets being dropped because of network congestion. Since the available bit-rate c n varies with time, rate control is important for timely delivery of video packets. We take one-way delay t n as half of RTT τ n, i.e., t n := τn 2. Following the derivation in [1, 17], we relate the one-way delay t n over access network n and its remaining bandwidth c n r n as: t n = α n c n r n, (1) where parameter α n is estimated from the past observations of RTTτ n and residue bandwidthc n r n by linear regression. Let p n be the packet loss probability over access network n, which corresponds to random losses and packets missing their playout deadlines. We assume that packet losses are statistically independent across different access networks and across different transmissions, and we writep n = g n (c n,r n ), where g n (, ) models the packet loss rate. We assume that g n (, ) is increasing in r n and decreasing in c n. By considering a generalg n (, ) function we can accommodate various queueing models [18], e.g., the M/M/1 queueing model or finite-length queueing models such as M/M/1/K and G/D/1/K. The actual world is far more complicated than a single-hop queueing model, since it entails streaming over multiple hops and varying routes. Nevertheless, the M/M/1 model has been observed to yield a good approximation in previous work [1, 17], while our simulation results (Sec. ) also confirm that it is quite effective and leads to significant performance improvements. Therefore, we consider the M/M/1 model in the rest of the paper, but our analysis is also applicable to other network models. 2

3 Under the assumption of the M/M/1 model, we writep n = g n (c n,r n ) = e t/tn [18], where t is the playout deadline and t n is the average one-way delay. The parameter t is application-dependent. Combining this equation with (1), we get p n = e t (cn rn) αn. (2) 3.2. Scalable Video Streams The H.264/SVC [8] standard employs hierarchical prediction structure among frames within the same group of pictures (GoP). That is, video frames are divided into a temporal base layer and multiple temporal enhancement layers. Frames in the temporal base layer use only the temporal base layer frame of the previous GoP for prediction, while frames in the temporal enhancement layers use the two neighboring frames in lower temporal layers for predictions. Fig. 2 illustrates this prediction structure. Each frame consists of multiple quality layers. There are two types of quality scalability: coarse-grained scalability (CGS) and medium-grained scalability (MGS). We consider MGS layers in this work. H.264/SVC streams are divided into Network Abstraction Layer units (NALUs), and each NALU g m,q is identified by the frame number m, 1 m M, and the quality layer q, q Q, where M is the number of frames considered in each optimization problem and Q + 1 is the number of quality layers. We consider M to be a multiple of the GoP size. For any frame m, NALU g m, carries the basic quality representation, and NALU g m,q, where < q Q, contains quality enhancement for that frame. We let s m,q be the size of NALU g m,q. NALU g m,q, 1 q Q, is decodable if and only if all NALUs of lower quality layers (g m,q, q < q) are received on time and decodable. NALU g m, is decodable if its hierarchical prediction parents are successfully received on time. We letp m be the ancestor frames 2 of framem. The two immediate parents of frame m are denoted by p m,1 and p m,2. With the above notations, we can describe our multihomed scalable video streaming problem as determining which NALUs to send, and associate each NALU with an access network. We let x m,q,n be a boolean variable indicating whether we send g m,q over access network n: x m,q,n = { 1, if we send gm,q over network n, otherwise. We assume that the packet loss rates of access networks are sufficiently low so that sending a NALU over multiple access networks results in no advantage, while incurring higher network load due to duplicated data transfer. This assumption is reasonable because many modern access networks implement forward error correction (FEC) and automatic repeat 2 In this paper, we use bold symbols to represent vectors. (3) request (ARQ) at the data-link layer. These mechanisms are not controlled by the video streaming system at the application layer. We define x m,q := N n=1 x m,q,n, (4) to be a binary variable with value 1 if NALUg m,q is sent over some network, and otherwise. Next, we develop our distortion model to estimate the distortion of a substream extracted from a scalable stream. We use mean square error (MSE) as the distortion metric and denote the total distortion of frame m by d m, which can be divided into two components: truncation distortion e m and drifting distortiony m. We let d m = e m +y m. Truncation distortion, e m, refers to the quality degradation due to dropping NALUs of frame m. We let ˆδ m be the full-quality distortion of frame m, which is achieved when g m,q for allq =,1,...,Q are received on time. We letδ m,q ( q Q) be the additional distortion introduced by dropping NALU g m,q. Because of the dependency among MGS layers, to decode g m,q, all NALUs g m,q, where q < q must have been decoded. Following the definition of (3), we can write truncation distortion as: e m = ˆδ m + Q q=( 1 xm,q ) δm,q, where () x m,q := q q x m,q = min q qx m,q. (6) The value of x m,q is equal to 1, if and only if all NALUs of frame m with quality layers lower than or equal to q are received. Drifting distortion,y m, refers to the distortion caused by inter-frame predictions due to imperfect reconstruction of ancestor frames in P m. We consider an abstract model of the form y m = f m (e Pm ), where f m is increasing in each argument on R Pm +. Sun et al. [1] propose to use an increasing bilinear distortion model: y m = f m (e pm,1,e pm,2 ) = ζ m, +ζ m,1 e pm,1 +ζ m,2 e pm,2 +η m e pm,1 e pm,2. (7) In this model, ζ m,1 and ζ m,2 are nonnegative constants that are computed from the known fraction of inter-coded macroblocks in framem. The parametersζ m, andη m are derived from empirical data and η m is assumed nonnegative. We generalize this bilinear model into a degree-2 polynomial model: y m = f m (e pm,1,e pm,2 ) = ζ m +η m,1 e pm,1 +η m,2 e pm,2 + κ m,1 e 2 p m,1 +2κ m,2 e pm,1 e pm,2 +κ m,3 e 2 p m,2, (8) where ζ m, η, and κ are model parameters. We consider a convex increasing drifting distortion function ( by adding the ) κm,1 κ m,2 constraints that η, κ are nonnegative and κ m,2 κ m,3 3

4 is positive semidefinite. We call this generalized model as degree-2 polynomial model. Liang et al. [19] suggest that the truncation error of a frame propagates to its descendants in the partial ordering of inter-frame prediction in the same GoP, in a linear fashion. Inspired by this, we propose another generalized model y m = f m (e Pm ) = γ m,m + k P m γ m,k e k, (9) where γ m,k are nonnegative parameters. We refer to this model as multi-scale linear model. In Sec..2, we empirically compare the two proposed distortion models against a model in the literature Optimization Problem We formulate the multihomed scalable video streaming problem as one of finding the x m,q,n values to maximize video quality at the client, i.e., minimize the total expected distortion, under current network conditions. Let F be the frame rate in frames-per-second (fps). The average transport stream rate for network n, in a time interval of length M F, is r n = F M M m=1 Q q= s m,qx m,q,n. (1) The rate can be used to estimate packet loss probability p n over networknby means of the network model (2). Under the statistical independence assumption, the expected delivery probability of NALU g m,q, denoted (by some abuse of notation) by x m,q [,1], can be calculated as: x m,q = N n=1 (1 p n)x m,q,n. (11) In writing (11), we assume that each access network n behaves like a binary channel with loss probability p n for each packet, and that each NALU comprises a single packet for brevity. In the light of (11) we can rewrite x m,q in (6) and model the truncation distortion using (). The joint rate control and stream adaptation problem can be written as an optimization problem of finding x := {x m,q,n }: min x M m=1 d m (12a) F M Q s.t. r n = M m=1 q= s m,qx m,q,n, (12b) p n = e t(cn rn)/αn, (12c) N x m,q = n=1 (1 p n)x m,q,n, (12d) x m,q = q q x m,q, (12e) e m = ˆδm + Q q= (1 x m,q)δ m,q, (12f) y m = f m (e Pm ), (12g) d m = e m +y m, (12h) x m,q,n {,1},m = 1,...,M,q =,...,Q, n = 1,...,N, N n=1 x m,q,n 1. (12i) (12j) In this problem, rate control is performed through (12c). This is a form of proactive congestion control, in the sense that it seeks to avoid causing network congestion, as opposed to the responsive nature of TCP-like rate control algorithms. Assuming thatf m is increasing in each argument, the objective function is increasing in p n, for fixed x. It is decreasing in x m,q for each m = 1,2,...,M and q =,1,...,Q. The objective function is increasing in e m and y m for each m. Based on these properties, we can replace the equality constraints in (12c), (12d), and (12g) with,, and inequality constraints, respectively. This yields an equivalent formulation with no nonlinear equality constraints. We note that (12) is an integer program [2], and its statespace has a cardinality of 2 MQN, which renders exhaustive search intractable for actual applications. While dynamic programming can be employed for computing the optimal solution, doing so still leads to exponential complexity, and prohibitively long running time. Remark 1. The formulation in (12) can account for the case that NALU g m,q comprises multiple packets, say U m,q packets. Let x m,q,u,n be 1 if the u th packet of NALU g m,q is sent over access network n, and else. We then substitute x m,q,n with x m,q,u,n in (12i), (12j) and replace (12d), (12e) with x m,q,u = N n=1 (1 p n)x m,q,u,n, (13) x m,q = Um,q q q u=1 x m,q,u. (14) Typically, U m,q is a function of NALU size s m,q. For example, for a path with maximum payload lengthθ, the streaming server may send NALU g m,q with U m,q = sm,q θ. The algorithms proposed in Sec. 4 can be readily extended by updating the distortion model. 4. HEURISTIC ALGORITHMS In this section, we present three heuristic algorithms Simple Rate-Distortion Optimization By ignoring the drifting distortion, we propose a heuristic algorithm that first sorts NALUs g m,q on their importance. Then, it sequentially schedules the NALUs until the access networks are fully loaded, i.e., right before their loss probabilities exceed a desired maximal value P max. We call this algorithm Simple Rate-Distortion Optimization (), and we give its pseudocode in Figure 3. The algorithm takes the maximum packet loss rate as an input. In line 2, it sorts NALUs on the ratio of potential quality improvement δ m,q and size s m,q. The for-loop between lines 3 7 iteratively finds the least loaded access network, and transmits the next unsent NALU over it. The algorithm returns in line if the maximum packet loss rate is exceeded, and in line 7 if all NALUs have been sent. Computa- 4

5 1. let x = {x m,q,n = m,q,n} 2. sort g m,q on δm,q s m,q 3. for ˆn = argmin N n=1p n 4. let gˆm,ˆq be the next unsent NALU. if sending gˆm,ˆq on ˆn causes pˆn > P max return x 6. else update x withxˆm,ˆq,ˆn = 1 7. if no more unsent NALU return x Fig. 3. Simple Rate Distortion Optimization algorithm. tional complexity of the algorithm is dominated by the sorting in line 2, on the order ofo ( M(Q+1)log[M(Q+1)] ) Progressive Rate-Distortion Optimization The algorithm assumes the drifting distortion is insignificant, which is less accurate for videos with higher temporal correlation. We propose a new algorithm following the observation that sending one more NALU g m,q concurrently incurs positive and negative impacts on the total distortion. More specifically, sending NALU g m,q over access network n leads to: (i) lower truncation distortion for frame m and lower drifting distortion for its descendants, and (ii) higher distortion for frames with NALUs already assigned to access network n, since packet loss rate p n is an increasing function of the network load. We let b m,q,n be the net distortion impact of sending NALU g m,q over access network n on top of of what have been sent. The new algorithm is referred to as Progressive Rate- Distortion Optimization (): it follows the video dependency structure and iteratively sends more NALUs by selecting the NALU that would reduce total distortion the most. The algorithm stops if all NALUs have non-positive net distortion impact values, or if there is no unsent NALU. By leveraging on the dependency structure, we can largely reduce the number ofb m,q,n to be computed. More precisely, our algorithm considers only the immediately decodable NALUs at each step, that is to say the NALUs with all their ancestors transmitted earlier. We give the pseudocode of our proposed algorithm in Fig. 4. The loops starting in lines 2,, and 6 have at most M(Q + 1), M, and N iterations, respectively. Furthermore, line 7 can be computed by scanning through allm(q+1)n NALUs only once. Hence, the algorithm has a polynomial time complexity of O ( M 3 (Q+1) 2 N 2) Hybrid Rate-Distortion Optimization We propose a Hybrid Rate-Distortion Optimization (HRDO) algorithm, which uses to bootstrap a solution, and then applies to send more NALUs. HRDO stops when there 1. letx = {x m,q,n = m,q,n} 2. forever 3. letg d be all immediately decodable NALUs 4. ifg d is empty return x. forg m,q g d 6. for n = 1 ton 7. compute b m,q,n based on x 8. letbˆm,ˆq,ˆn /sˆm,ˆq b m,q,n /s m,q m,q,n 9. ifbˆm,ˆq,ˆn return x 1. update x withxˆm,ˆq,ˆn = 1, update g d Fig. 4. Progressive Rate-Distortion Optimization algorithm. is no immediately decodable NALUs leading to distortion reduction.. EVALUATION.1. Network and Video Trace Collection We use Abing [16] to periodically measure ABR and RTT values between hosts on two networks. We chose Abing because it converges fast and is light-weight [21]. We collect network traces between Deutsche Telekom Laboratories (in Berlin) and Stanford University. Three access networks are considered: Ethernet, 82.11b, and 82.11g. We configureabing to take a measurement every two seconds for two hours. Parts of the network traces have been used in our previous studies [1, 11]. We consider four 1-sec video sequences: City, Soccer, Crew, and Harbour. These sequences are in 4CIF (74x76) resolution at 3 fps. We use JSVM Reference Software (version ) to encode each sequence into a scalable stream with a GoP size of eight and eight MGS layers. We tested different numbers of MGS layers and found that number of MGS layers does not affect coding efficiency substantially. Fig. illustrates that, compared to Q = 2, Q = 8 only results in 7.% rate increase. Once we get the scalable streams, we parse them for the NALU sizes m,q..2. Video Distortion Model Validation For each scalable stream, we estimate the truncation distortion model parameters as follows. We first decode the complete scalable stream and compute the full-quality distortion ˆδ. Next, we truncate NALUs g m,q for each frame m = 1,2,...,M with q =,1,...,Q, but we keep all NALUs of frames in temporal layers lower than m. We then decode the truncated video stream to compute δ m,q. We preserve the complete frames in lower temporal layers to prevent any drifting distortion. To estimate the drifting distortion model parameters, we decode each scalable stream 32 times with random ancestor

6 Table 1. Goodness-of-fit comparison in RMSE. Seq. Bilinear Multi-scale Linear Deg-2 Poly. Crew Harbour Soccer City frame truncations, and fit the samples to the distortion models using Matlab (version R29b). We use the same samples for the proposed degree-2 polynomial model of (8) and multiscale linear model of (9). We then compare their goodnessof-fit results against the bilinear model of (7) in Table 1. This table shows that the proposed models outperform the model in [1]. The multi-scale linear model provides the best fit for all sequences except City, therefore we use this model in simulations. To validate the model accuracy, we randomly drop NALUs from each scalable video stream and decode it to get the empirical distortion. We then use the multi-scale linear model of (9) to estimate the distortion. Fig. 6 plots a sample time period of Crew, which clearly shows that the proposed model closely follows the empirical distortion..3. Setup We evaluate our proposed algorithms using the NS-2 simulator [22] by implementing a multihomed streaming server which supports the,, and HRDO algorithms. These algorithms are implemented as Matlab subroutines. For comparison, we consider the rate control algorithms defined in DCCP [1], a modern transport protocol designed for video streaming. We use a public DCCP implementation [23] in NS-2, with two standard rate control algorithms: (i) TCPlike algorithm that implements window-based TCP rate control and (ii) TFRC (TCP-Friendly Rate Control) algorithm that is an equation based algorithm achieving long-term TCP fairness. We have implemented a multihomed DCCP streaming server that sets up a connection over each access network. This server iterates through each DCCP connection and transmits NALUs from lower to higher quality layers until reaching the rate limit computed by the congestion control algorithms. We refer to the DCCP streaming server with TCPlike rate control as DCCP-TCP and the one with TFRC rate control as DCCP-TFRC. We simulate multihomed video streaming sessions for random starting times in network traces of the four considered video sequences. For each session, the simulator adjusts the capacity and delay of each access network following the network traces, and it rewinds video sequences once their ends are reached. We use the NS-2 traffic generators to add background traffic over each access network at a rate between 3% to 9% of its available bandwidth. We also implement Abing in NS-2 for ABR and RTT measurements. We choosem = 32 for our optimization problem, and set the playout deadline t = 1 sec. For the algorithm, the maximum packet loss ratep max is set to 1%. The maximum UDP packet size is set to 1 bytes. We run the simulations with four video sequences. For each setup, we use each algorithm to solve the optimization problem 18 times, and report average results. We consider four measures of performance: video quality in PSNR (Peak Signal-to-Noise Ratio), streaming rate, packet delivery delay, and running time..4. Results Benefits of Multihoming. We use the DCCP streaming server with different number of access networks and report the results from City with 3% background traffic. We run the DCCP rate control algorithms with one, two, and three access networks and compute the video quality. We plot sample results for a 6-sec period using DCCP-TCP in Fig. 7, while results from DCCP-TFRC are similar. This figure shows that multihoming can significantly increase video quality and reduce the number of quality fluctuations. Video Quality. We compare the video quality achieved by the proposed and algorithms against DCCP- TCP and DCCP-TFRC with 3% background traffic. In Fig. 8(a) we plot the video quality achieved using each algorithm for a 6-sec sample period. We observe that both DCCP-TCP and DCCP-TFRC suffer from sudden quality drops and that the proposed algorithms achieve high video streaming quality. We report the aggregate video quality for different video sequences in Fig. 8(b). The proposed algorithms outperform the DCCP rate control algorithms by at least 1 db in video quality. Streaming Rate and TCP-Friendliness. DCCP rate control algorithms are designed to be TCP-friendly. We report the streaming rates for different algorithms with 3% background traffic. The simulation results (figures not shown due to the page limitations) indicate that the proposed algorithms lead to smooth streaming rates, comparable to the average rates of the DCCP rate control algorithms. In Fig. 9, we present the average streaming rate for all considered video sequences and algorithms. This figure shows that the and algorithms result in almost the same streaming rates as DCCP- TCP and DCCP-TFRC, and hence are TCP-friendly. Packet Delivery Delay. We present the average packet delivery delay for all video sequences under 3% background traffic in Fig. 1. Our proposed algorithms result in short packet delivery delays, at least 2. seconds shorter than DCCP (or 83% delay reduction). This, in turn, shows that the inferior video quality of DCCP rate control algorithms is partially due to longer packet delivery delays causing video packets to miss their playout deadlines. Trade-off between Optimality and Computational Complexity. To throughly evaluate the proposed,, and HRDO algorithms, we stream City with them under vari- 6

7 Rate Increase (%) City Crew 1 Harbour Soccer No. MGS Layers Distortion in MSE Estimated Actual Frame Number Quality in PSNR (db) Net 2 Nets 3 Nets Time (sec) Fig.. Rate increase of different number of MGS layers. Fig. 6. Sample distortion model accuracy from Crew. Fig. 7. Video quality achieved by different numbers of access networks. Quality in PSNR (db) DCCP-TCP DCCP-TFRC Time (sec) (a) Quality in PSNR (db) DCCP-TCP DCCP-TFRC City Soccer Crew Harbour Video Sequence Fig. 8. Video quality comparison: (a) City and (b) overall results. (b) Streaming Rate (Mbps) DCCP-TCP DCCP-TFRC City Soccer Crew Harbour Video Sequence Fig. 9. Streaming rate comparison from all video sequences. ous background traffic loads from % to 9% 3. We plot the video quality achieved by the proposed algorithms at different background traffic loads in Fig. 11(a). This figure shows that when the background traffic is not significant, performs almost as good as, but the performance gap becomes nontrivial (about 1 db) when background traffic is increased to 9%. HRDO performs slightly worse than, unless the bandwidth is highly saturated. We plot the running time of different algorithms in Fig. 11(b). We observe that runs in real-time (between 1 2 msec), while takes significantly longer time to finish, and HRDO has an intermediate running time. Fig. 11 shows the trade-off between optimality and computational complexity: results in better video quality, but has higher complexity, while runs faster, but leads to lower video quality. The HRDO algorithm depicts a good trade-off of optimality for complexity. 6. CONCLUSIONS We have addressed the problem of streaming scalable videos over multiple access networks to a client, based on an optimization framework. The objective is to maximize the per- 3 In our experiments, we found that these algorithms result in similar performance when the background traffic load is lower than %. ceived video quality at the client subject to constraints on network conditions and video characteristics. We have formulated the problem into an integer programming problem, and have proposed two heuristic algorithms: the algorithm assumes the drifting distortion is insignificant and has a very low time complexity, while the algorithm employs an elaborate distortion model for better video quality at the expense of longer running time. We have also proposed a hybrid HRDO algorithm with good balance between optimality and computational complexity. We have evaluated all the algorithms using NS-2 simulator with real network and video traces. The simulation results have shown that the proposed algorithms outperform the rate control algorithms defined in the DCCP standard [1]. In particular, the proposed algorithms: (i) result in higher video quality, (ii) is TCP-friendly, and (iii) incur short packet delivery delays. The present work can be extended along several directions. We plan to develop algorithms based on suboptimal convex programs for the joint rate and distortion optimization problem. We plan to integrate the packet loss rates of wireless networks into our formulation, and further assign different FEC rates to different NALUs. Another direction is generalizing the optimization problem for multiple streaming servers competing for the same access networks. 7

8 Packet Delay (sec) DCCP-TCP DCCP-TFRC City Soccer Crew Harbour Video Sequence Fig. 1. Packet delivery delay comparison. Quality in PSNR (db) HRDO Background Traffic (%) (a) Running Time (sec) HRDO Background Traffic (%) Fig. 11. Trade-off between (a) optimality and (b) computational complexity. (b) 7. REFERENCES [1] X. Zhu, P. Agrawal, J. Singh, T. Alpcan, and B. Girod, Distributed rate allocation policies for multihomed video streaming over heterogeneous access networks, IEEE Transactions on Multimedia, vol. 11, no. 4, pp , June 29. [2] T. Alpcan, J. Singh, and T. Basar, Robust rate control for heterogeneous network access in multihomed environments, IEEE Transactions on Mobile Computing, vol. 8, no. 1, pp. 41 1, January 29. [3] J. Apostolopoulos and M. Trott, Path diversity for enhanced media streaming, IEEE Communications Magazine, vol. 42, no. 8, pp. 8 87, August 24. [4] F. Hartung, U. Horn, J. Huschke, M. Kampmann, T. Lohmar, and M. Lundevall, Delivery of broadcast services in 3G networks, IEEE Transactions on Broadcasting, vol. 3, no. 1, pp , March 27. [] AT&T faces, percent surge in traffic, http: // php/38431, 29. [6] T-Mobile s growth focusing on 3G, http: //connectedplanetonline.com/wireless/ news/t-mobile-3g-growth-13, 29. [7] J. Xin, C. Lin, and M. Sun, Digital video transcoding, Proceedings of the IEEE, vol. 93, no. 1, pp , January 2. [8] H. Schwarz, D. Marpe, and T. Wiegand, Overview of the scalable video coding extension of the H.264/AVC standard, IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 9, pp , September 27. [9] M. Wien, H. Schwarz, and T. Oelbaum, Performance analysis of SVC, IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 9, pp , September 27. [1] E. Kohler, M. Handley, and S. Floyd, Datagram congestion control protocol (DCCP), RFC 434, March 26. [11] J. Singh, T. Alpcan, P. Agrawal, and V. Sharma, An optimal flow assignment framework for heterogeneous network access, in Proc. of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 7), Helsinki, Finland, June 27, pp [12] M. Hefeeda and C. Hsu, Rate-distortion optimized streaming of fine-grained scalable video sequences, ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 4, no. 1, pp. 2:1 2:28, January 28. [13] I. Amonou, N. Cammas, S. Kervadec, and S. Pateux, Optimized rate-distortion extraction with quality layers in the scalable extension of H.264/AVC, IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 9, pp , September 27. [14] H. Mansour, V. Krishnamurthy, and P. Nasiopoulos, Channel aware multiuser scalable video streaming over lossy underprovisioned channels: Modeling and analysis, IEEE Transactions on Multimedia, vol. 1, no. 7, pp , November 28. [1] J. Sun, W. Gao, D. Zhao, and W. Li, On rate-distortion modeling and extraction of H.264/SVC fine-granular scalable video, IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 3, pp , March 29. [16] Abing project page, stanford.edu/tools/abing/. [17] X. Zhu, E. Setton, and B. Girod, Congestion-distortion optimized video transmission over ad hoc networks, Signal Processing: Image Communication, vol. 2, no. 8, pp , September 2. [18] D. Gross, J. Shortle, J. Thompson, and C. Harris, Fundamentals of Queueing Theory, Wiley-Interscience, 4th edition, 28. [19] Y. Liang, J. Apostolopoulos, and B. Girod, Analysis of packet loss for compressed video: Effect of burst losses and correlation between error frames, IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 7, pp , July 28. [2] S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 1st edition, 24. [21] J. Navratil and R. Cottrell, ABwE: A practical approach to available bandwidth estimation, in Proc. of Passive and Active Measurement Workshop (PAM 3), La Jolla, CA, April 23. [22] The network simulator, nsnam/ns/. [23] N. Mattsson, A DCCP module for NS-2, M.S. thesis, Department of Computer Science and Electrical Engineering, Lulea Tekniska University, 24. 8