CLS

Viewport adaptive streaming is emerging as a promising way to deliver high quality 360-degree video. It is still a critical issue to predict user's viewpoint and deliver partial video within the viewport. Current widely-used motion-based or content-saliency methods have low precision, especially for long-term prediction. In this paper, benefiting from data-driven learning, we propose a Cross-user Learning based System (CLS) to improve the precision of viewport prediction. Since users have similar region-of-interest (ROI) when watching a same video, it is possible to exploit cross-users' ROI behavior to predict viewport. We use a machine learning algorithm to group users according to historical fixations, and predict the viewing probability by the class. Additionally, we present a QoE-driven rate allocation to minimize the expected streaming distortion under bandwidth constraint, and give a Multiple-Choice Knapsack solution. Experiments demonstrate that CLS provides 2dB quality improvement than full-image streaming and 1.5 dB quality improvement than linear regression (LR) method. On average, the precision of viewpoint prediction improve 15% compared with LR.