Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming
Jie Li, Cong Zhang, Zhi Liu, Wei Sun, Qiyue Li
Abstract
Point cloud video is the most popular representation of hologram, which is the medium to precedent natural content in VR/AR/MR and is expected to be the next generation video. Point cloud video system provides users immersive viewing experience with six degrees of freedom (6DoF) and has wide applications in many fields such as online education and entertainment. To further enhance these applications, point cloud video streaming is in critical demand. The inherent challenges lie in the large size by the necessity of recording the three-dimensional coordinates besides color information, and the associated high computation complexity of encoding/decoding. To this end, this paper proposes a communication and computational resource allocation scheme for QoE-driven point cloud video streaming. In particular, with the goal to maximize the defined QoE by selecting proper quality levels (uncompressed tiles at different quality levels are also considered) for each partitioned point cloud video tile, we formulate this into an optimization problem under the limited communication and computational resources constraints and propose a scheme to solve it. Extensive simulations are conducted and the simulation results show the superior performance of the proposed scheme over the existing schemes.