On the use of location window in geo-intelligent HTTP adaptive video streaming

Abstract

HTTP adaptive video streaming has become the de facto standard for media data delivery in the Internet. Mobile users are increasingly accessing video streaming services while traveling in fast-moving vehicles (e.g., public transport). The inherent high-speed mobility in these scenarios escalates bandwidth uncertainty and seriously degrades the performance of HTTP adaptive video streaming. This paper proposes a location window based geo-intelligent adaptive streaming algorithm, which adapts to the geo-spatial bandwidth variations experienced by a fast-moving user by adjusting the quality of the next chunk based on the estimated bandwidth at the next X locations of the mobile user. In order to realize geo-intelligence, we introduce a neural network model for accurately creating bandwidth maps that store location-specific bandwidth knowledge. By incorporating both these contributions in conjunction with real-world mobile broadband bandwidth traces from a metropolitan area, we present a systematic study to explore the effects of varying the size of the location window on the user-perceived Quality of Experience (QoE). The evaluation results demonstrate that an optimum location window can be identified, which can almost entirely eliminate playout buffer underruns, thus leading to a smooth and high-quality streaming experience.