Adaptive Multicasting for MEC-Assisted 360-Degree Video Streaming

With advancements in hardware and communication technologies, the demand for 360-degree video applications has surged. However, the overall spectral efficiency is compromised due to the inability of current multicast schemes to accurately identify 360-degree video users with similar characteristics, resulting in improper user grouping. To address these issues, we propose Hcast360, an adaptive user-correlation-based 360-degree video multicast strategy, in which we designed a user correlation metric based on the throughput difference between multicast and unicast to determine the feasibility of multicast among users. This strategy accurately assigns users to appropriate multicast groups, significantly improving the utilization efficiency of wireless resources. We decompose the 360- degree video multicast problem into three sub-problems: user grouping, resource allocation, and bitrate adaptation. To solve these problems, we propose an adaptive grouping algorithm that assigns users to suitable multicast groups without predefining the number of multicast groups. Finally, we employ a genetic algorithm to determine the bitrate for 360-degree video tiles. Experimental results show that our algorithm can improve users' Quality of Experience (QoE) significantly, achieving noticeable gains without any increase in bandwidth usage.