FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming over HTTP
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In this paper, we explore streaming video over HTTP. The problem is formulated as a non-convex optimization problem which is constrained by the predicted bandwidth, chunk deadlines, available video rates, and buffer occupancy. The objective is to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among different chunks for the minimum re-buffering time. We propose FastScan, a low complexity algorithm that solves the problem, and show that the solution is optimal when every quality level is encoded in Constant Bit Rate (CBR). Online adaptations for dynamic bandwidth environments are proposed with imperfect bandwidth prediction. Results of real experiments driven by real Variable Bit Rate (VBR) encoded video, video platform system (dash.js), and cellular bandwidth traces of a public dataset reveal the robustness of the online version of FastScan algorithm and demonstrate its significant performance improvement as compared to the state-of-the-art video streaming algorithms. For example, on an experiment conducted over 100 real cellular bandwidth traces of a public dataset that spans different bandwidth regimes, our proposed algorithm (FastScan) achieves the minimum re-buffering (stall) time and the maximum average playback rate in every single trace as compared to the original dash.js rate adaptation scheme, Festive, BBA, RB, and FastMPC algorithms.
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