Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming

TL;DR

This paper proposes a resource allocation scheme for point cloud video streaming that optimizes communication and computation resources to enhance user experience, demonstrating superior performance through extensive simulations.

Contribution

It introduces a novel QoE-driven resource allocation method specifically designed for the unique challenges of point cloud video streaming.

Findings

Improved system resource utilization compared to existing schemes

Enhanced quality of experience for users in streaming scenarios

Demonstrated effectiveness through extensive simulations

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 and has wide applications in many fields such as online education, 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. To this end, this paper proposes a communication and computation resource allocation scheme for QoE-driven point cloud video streaming. In particular, we maximize system resource utilization by selecting different quantities, transmission forms and quality level tiles to maximize the quality of experience. Extensive simulations are conducted and the simulation results show the superior performance over the existing schemes