CMU-VP: Cooperative Multicast and Unicast With Viewport Prediction for VR Video Streaming in 5G H-CRAN

Virtual reality (VR) is commonly regarded as one of 5G killer-applications. Transmission efficiency and quality of experience (QoE) are the most concerning issues for VR video streaming in 5G networks. Several multicast approaches were proposed to address these issues regardless of variance of personal viewports. In this paper, we explore a novel scheme combining multicast and unicast sessions in heterogeneous cloud-radio access networks (H-CRAN), in which a basic version of the video is transmitted to all users through the g-NB in a multicast session, and tiles of enhanced-version are transmitted to each viewer in a unicast session through its stationed remote radio head (RHH). To ensure the real-time content delivery, a user's viewport is predicted using a method based on historical trajectories and similarity of motion behavior, and then the tiles of predicted viewport in a version dependent on the channel quality are sent to the user in the unicast session. The scheme is formulated into a mixed-integer nonlinear problem (MINLP), and two near-optimal solutions are proposed to solve it by applying greedy approach and approximate approach, respectively. The simulation results show that our proposed scheme ensures better QoE under constrained bandwidth, and the proposed near-optimal solutions can efficiently solve the problem with low complexity and comparable performance.