Resource Allocation for Multihomed Scalable Video Streaming to Multiple Clients

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.