Cross-Layer Assisted Early Congestion Control for Cloud VR Services in 5G Edge Network

PubDate: Jul 2023

Teams: Chinese Academy of Sciences;University of Chinese Academy of Sciences

Writers: Wanghong Yang, Wenji Du, Baosen Zhao, Yongmao Ren, Jianan Sun, Xu Zhou

PDF: Cross-Layer Assisted Early Congestion Control for Cloud VR Services in 5G Edge Network

Abstract

Cloud virtual reality (VR) has emerged as a promising technology, offering users a highly immersive and easily accessible experience. However, the current 5G radio access network faces challenges in accommodating the bursty traffic generated by multiple cloudVR flows simultaneously, leading to congestion at the 5G base station and increased delays. In this research, we present a comprehensive quantitative analysis that highlights the underlying causes for the poor delay performance of cloudVR flows within the existing 5G protocol stack and network. To address these issues, we propose a novel cross-layer informationassisted congestion control mechanism deployed in the 5G edge network. Experiment results show that our mechanism enhances the number of concurrent flows meeting delay standards by 1.5x to 2.5x, while maintaining a smooth network load. These findings underscore the potential of leveraging 5G edge nodes as a valuable resource to effectively meet the anticipated demands of future services.