CUB360: Exploiting Cross-Users Behaviors for Viewport Prediction in 360 Video Adaptive Streaming

摘要

To ensure 360-degree video's continuous playback and reduce the bandwidth waste, predicting user's future fixation is indispensable. However, existing methods concentrate either on user's motion information or content information. None of them consider users watching behaviors' inconsistency which embodies user's attention distribution more explicitly. So in this paper, we exploit Cross-Users Behaviors for viewport prediction in 360-degree video adaptive streaming, namely CUB360, trying to concurrently consider user's personalized information and cross-users behaviors information to predict future viewport. Besides, we use a QoE-driven framework to optimize existing video streaming approaches and propose a general algorithm aiming at solving the NP problem at a low complexity. Extensive experimental results over real datasets demonstrate that compared with traditional adaptive streaming method, our proposal can significantly boost the prediction accuracy by 20.2% absolutely and 48.1 % relatively. Besides, the mean quality can get 30.28% gain while quality variance can be reduced by 29.89%.