LiveROI

Abstract

Virtual reality (VR) streaming can provide immersive video viewing experience to the end users but with huge bandwidth consumption. Recent research has adopted selective streaming to address the bandwidth challenge, which predicts and streams the user's viewport of interest with high quality and the other portions of the video with low quality. However, the existing viewport prediction mechanisms mainly target the video-on-demand (VOD) scenario relying on historical video and user trace data to build the prediction model. The community still lacks an effective viewport prediction approach to support live VR streaming, the most engaging and popular VR streaming experience. We develop a region of interest (ROI)-based viewport prediction approach, namely LiveROI, for live VR streaming. LiveROI employs an action recognition algorithm to analyze the video content and uses the analysis results as the basis of viewport prediction. To eliminate the need of historical video/user data, LiveROI employs adaptive user preference modeling and word embedding to dynamically select the video viewport at runtime based on the user head orientation. We evaluate LiveROI with 12 VR videos viewed by 48 users obtained from a public VR head movement dataset. The results show that LiveROI achieves high prediction accuracy and significant bandwidth savings with real-time processing to support live VR streaming.