DBABP: Robust Rate Adaptation Algorithm for SVC Video Streaming
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Video streaming today accounts for up to 55% of mobile traffic. In this paper, we explore streaming videos encoded using Scalable Video Coding scheme (SVC) over highly variable bandwidth conditions such as cellular networks. SVC's unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing a concave function of the playback quality averaged over the chunks, and minimizing the number of quality switches. Second, we develop Deadline and Buffer Aware Bin Packing Algorithm (DBABP), a novel algorithm that solves the proposed optimization problem. Moreover, we show that DBABP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online DBABP) where several challenges are addressed including handling bandwidth prediction errors, and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared to the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today's mobile devices.
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