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. The online adaptations for dynamic bandwidth environments are proposed with imperfect available bandwidth prediction. The 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, FastMPC, and Pensieve algorithms.

Elgabli Anis, Aggarwal Vaneet

Publication type:
A1 Journal article – refereed

Place of publication:

adaptive bit rate (ABR), average video quality, non convex optimization, stall duration, Video streaming


Full citation:
A. Elgabli and V. Aggarwal, “FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming Over HTTP,” in IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 7, pp. 2240-2249, July 2020, doi: 10.1109/TCSVT.2019.2914388


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