Cross-Layer Assisted Early Congestion Control for Cloud VR Applications in 5G Edge Networks

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

Abstract: Cloud virtual reality (VR) has emerged as a promising technology, offering users a highly immersive and easily accessible experience. However, concurrent pulse VR flows can lead to significant congestion in 5G base stations, making network providers unable to guarantee the delay requirements for all users. Based on a comprehensive analysis of the poor delay per-formance of cloudVR flows within the existing 5G edge network, we propose a novel cross-layer congestion control mechanism that is assisted by access network status and flow characteristics. This mechanism is deployed within the 5G edge network and enables efficient global scheduling of concurrent flows. Experiment results show that our mechanism greatly optimizes the network delay in concurrent scenarios and guarantees the delay requirements of all users while avoiding network overload. Our work underscores the advantage of leveraging 5G edge nodes as a valuable resource to meet the anticipated demands of future services effectively.

External IDs:dblp:conf/wcnc/YangDZRSZ24