Probabilistic Viewport Adaptive Streaming for 360-degree Videos

Recently, there has been a significant interest towards 360-degree virtual reality (VR) video. However, it is a big challenge for them to stream over Internet for huge bit-rates. In this paper, we have designed a novel viewport adaptive streaming scheme for 360-degree videos with probabilistic viewport prediction and optimal segments prefetching by Dynamic Adaptive Streaming over HTTP (DASH). In this way, continuous and smooth video playback, low viewport prediction error and high PSNR are obtained. To avoid head-movement prediction error, a probabilistic viewport prediction model is proposed, which leverages the probability distribution of user's orientation. Further, an optimal segments prefetching method is implemented. Finally, we also implement our method in a real system. The numerous experiment results have demonstrated that the proposed method has achieved significant performance gains compared with the existing methods. Our related work also win the Runner-up in ICME 2017 DASH-IF Grand Challenge: Dynamic Adaptive Streaming over HTTP.