LOCA: Long-Term Optimization Based on Chunk-Level Analysis in Edge-Assisted Massive Mobile Live Streaming

This paper presents an edge-assisted massive mobile live streaming (MMLS) framework named LOCA, integrating chunk-level analysis and long-term optimization to design resource allocation, bitrate adaptation, and source selection strategies. The proposed method ensures sustained real-time video delivery while minimizing latency and communication costs. First, a chunk-level analysis of the entire process of video streaming is introduced, aiming at modeling fetch queue waiting time and rebuffering duration in each time slot. By embedding this mathamatical model into consideration, a long-term optimization is formulated to minimize rebuffering and communication overhead while maintaining high video qualities for massive users. Leveraging Lyapunov optimization, we transform this problem into a computationally tractable form. Further simplification via linearization achieves near-optimal solutions by adopting the mixed-integer linear programming method with enhanced computational efficiency. Simulation results demonstrate superior stability and long-term performance compared to the state-of-the-art and baseline methods, validating the framework’s efficacy in MMLS scenarios.