A Server-driven View-aware Point Cloud Video Streaming Framework

Point cloud video is an effective method to represent moving objects for metaverse applications. Real-time streaming of point cloud video can offer truly immersive experiments for metaverse users. However, point cloud videos have an extremely high data rate and demand significant processing resources for compression and rendering at the user device. To address the above challenges, this paper presents a novel server-driven view-aware point cloud video streaming system that can effectively reduce bandwidth requirements by combining hidden point removal and video-based point cloud encoding. Experiment results show that the proposed method can reduce the bandwidth requirement by up to 24% compared to the baseline method.