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Adaptive Computing and Multicasting Optimization for Live 360-Degree Video Streaming
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AI
DOI：10.1109/TVT.2025.3605289.png)
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Abstract
En 中文
Virtual reality (VR) technology is widely employed across various domains, with its applications expanding as Artificial Intelligence (AI) technologies advance, bringing new scenarios and functionalities to 360-degree video streaming. However, these applications impose substantial demands on computational power and communication capacity, along with increased sensitivity to computation and transmission delay. In this paper, we propose a joint optimization scheme of computing and multicasting for 360-degree video in Mobile Edge Computing (MEC) networks to maximize the long-term Quality of Experience (QoE) of multiple users. We solve the computing and multicasting optimization problem separately by dividing it into adaptive grouping and resource optimization subproblems. By implementing adaptive grouping, we reduce redundant computation and transmission, thereby improving the efficiency of limited resource utilization. We propose a Cooperative Bargaining Game (CBG)-based resource allocation algorithm for efficient resource management and a Lyapunov optimization-based bitrate adaptation algorithm for long-term performance optimization, enhancing users' QoE while minimizing playback freezing. Our experimental results demonstrate significant improvements in multi-user long-term QoE, average bitrate, and reduced rebuffering time, underscoring the effectiveness of our scheme in demanding scenarios.
Keywords:
Virtual reality (VR)
360-degree video
computing
multicasting
adaptive grouping
resource optimization
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W