FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming over HTTP
This addresses video streaming efficiency for users and platforms, but it is incremental as it builds on existing rate adaptation algorithms.
The paper tackles the problem of optimizing video streaming quality over HTTP by proposing FastScan, a low-complexity algorithm that minimizes re-buffering time and maximizes average playback rate, achieving the best performance in all 100 real cellular bandwidth traces compared to state-of-the-art methods.
This paper proposes and evaluates a novel algorithm for streaming video over HTTP. The problem is formulated as a non-convex optimization problem which is constrained by the predicted available 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. Online adaptations for dynamic bandwidth environments are proposed with imperfect available bandwidth prediction. Results of experiments driven by 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 considered state-of-the-art video streaming algorithms. For example, on an experiment conducted over 100 real cellular available bandwidth traces of a public dataset that spans different available 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 Bola, Festive, BBA, RB, and FastMPC, and Pensieve algorithms.