Predictive and Robust Field-of-View Selection for Virtual Reality Video Streaming

Virtual reality technology is rapidly evolving towards providing immersive user experience. By predicting user’s field-of-view (FoV) in advance, only transmitting content viewed by the user can help to meet the stringent requirements of delivering enhanced video quality. In this paper, a predictive and robust FoV selection algorithm is devised to dynamically identify a subset of video tiles, guided by the prediction error due to user’s stochastic head movement. Considering that the required data size to cover actual FoV is positively correlated with the prediction error, we construct a context space represented by the prediction error. A partition method of context space is exploited to discretize continuous context, where the prediction errors are classified effectively, and adaptive tile selection can be carried out. Then, a padding strategy is proposed by estimating the transmission gain of each tile in different prediction context, which improves the coverage of transmitted content around the true FoV at less bandwidth cost. Experimental results based on a real-world dataset demonstrate that the proposed algorithm can achieve dynamic FoV adjustment, and effectively improve user’s quality of experience.