Extensive-Form Game Theory-Based Content Caching in Edge Networks

Multi-access Edge Computing (MEC) enhances user experience by caching content at nearby base stations or edge servers, thereby reducing repetitive data retrieval and latency. However, edge servers face challenges such as high data storage costs and stringent network requirements, which conflict with their limited performance capabilities. To address these issues, cooperative caching among servers has emerged as a pivotal research area. In this paper, we first establish a model that links the quality of experience (QoE) with the quality of service (QoS). Then, queuing theory is used to estimate the impact of access rate on QoE. Second, we describe the server file caching decision issue as a type of extensive game problem. Finally, we propose an improved discrete binary particle swarm optimization (BPSO) algorithm to solve the optimal decision. Simulation results indicate that our algorithm ensures a higher hit rate and achieves the highest QoE compared to existing algorithms.