Resource Allocation for Multihomed Scalable Video Streaming to Multiple Clients Nikolaos M. Freris * , Cheng-Hsin Hsu * , Xiaoqing Zhu † , and Jatinder Pal Singh * * Deutsche Telekom R&D Laboratories, 5050 El Camino Real 221 Los Altos, CA 94022 † Cisco Systems Inc., 170 West Tasman Drive San Jose, CA 95134 Abstract—We consider multihomed scalable video streaming, where videos are transmitted by a single server to multiple clients over heterogeneous access networks. The specific problem that we address is to determine which video packets to transmit over each network, in order to minimize a cost function of the expected video distortion at the clients. We present a network model and a video model that capture the network conditions and video characteristics, respectively. We develop an integer program for deterministic packet scheduling. We propose different cost functions in order to provide service differentiation and address fairness among users. We propose several suboptimal convex problems for randomized packet scheduling, and study their performance and complexity. We propose an algorithm that yields a good performance and is suitable for real-time applications. We conduct extensive trace-driven simulations to evaluate the proposed algorithms using real network conditions and scalable video streams. The simulation results show that the proposed algorithm: (i) outperforms the rate control algorithms defined in the Datagram Congestion Control Protocol (DCCP) by about 10 dB, (ii) results in video quality, of 4.33 dB and 1.84 dB higher than the two heuristics developed in [1], (iii) runs efficiently, up to six times faster than one of the heuristics, and (iv) indeed can provide service differentiation among users. Index Terms—video streaming, quality optimization, rate con- trol, stream adaptation I. I NTRODUCTION Modern laptops and hand-held devices can access multi- ple networks with diverse and dynamic characteristics. For example, 3G data networks offer pervasive connectivity but may suffer from low network capacity [2], while Wireless Local-Area Networks (WLANs) can provide higher capacity but each access point only covers a small area. In multi- homed video streaming [3], [4], a video is concurrently sent over multiple access networks in order to achieve higher aggregate bandwidth, more pervasive connectivity, improved error resilience, and lower communication delays [5]. Several US mobile service providers have reported large data traffic increases in their 3G data networks [6], [7]; multihoming can help offload traffic from congested networks, in order attain better streaming quality, as well as lower transit costs for service providers. Arbitrarily splitting a video stream into multiple substreams and sending each substream over an access network may lead to degraded video quality and playout glitches; this is because transmitting a substream at a low rate may under- utilize the network resources, while transmitting at a rate close to the available bandwidth causes late packet delivery. Rate control based on measurements of available bit rate (ABR) 1 Measurement Tool Video Database Video Splitter Net Intf. Net Intf. Net Intf. Net Intf. Internet Interface Network Joint Algorithm Optimization ABR/RTT Tool ABR/RTT Tool Streaming Server ..... Video Assembler Scalable Decoder Video Assembler Scalable Decoder WLAN Cellular ...... U 1 Clients Access Networks ..... ..... N ABR/RTT Fig. 1. System architecture of a scalable video streaming system with U clients and N access networks. and round-trip time (RTT) needs to be used to achieve a good trade-off between throughput and delay. Once the bit rate of each substream is determined, the video stream must be adapted into the right format so that it can be delivered to the client in a timely fashion. Stream adaptation is typically realized by computationally demanding transcoding [8], [9]. In contrast, scalable video coding, such as the H.264/SVC standard [9], supports efficient stream adaptation and allows service providers to save expenses on deploying streaming servers and transcoders. 1 Scalable video streams, however, feature complex interdependencies among video packets, that stream adaptation must carefully account for. We study the joint rate control and scalable stream adap- tation problem for multiple clients 2 concurrently competing for the same access networks (cf. Fig. 1). We formulate an optimization problem to determine, for each client: (i) the streaming rate over each access network, (ii) the video packets to be transmitted, and (iii) the access network each video packet is sent over. Our contributions can be summarized as follows: • We formulate the rate control and stream adaptation problem as an integer program where the objective is to minimize a cost function of the expected video distortion. We propose different cost functions in order to provide service differentiation and address fairness among users. • We consider randomized packet scheduling by relaxing the integer program into real-valued optimization pro- 1 Despite a small cost on coding inefficiency, modern H.264/SVC coders are reported to significantly outperform previous scalable coding schemes, and even outperform some nonscalable coders such as MPEG-4 ASP (Advanced Simple Profile) [10]. 2 Throughout the paper, we use the terms client and user interchangeably.