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This paper presents a Field of View (FoV) aware caching strategy for Mobile Edge Computing (MEC)-enabled wireless Virtual Reality (VR) networks to ensure seamless real-time video delivery. The proposed approach caches and prefetches each VR user’s FoV at base stations (BSs) using decentralized and personalized federated learning (DP-FL) algorithms tailored to individual BSs. The DP-FL method personalizes content delivery while providing a probably approximately correct (PAC) guarantee on cache hit rates. To reduce communication overhead, a one-bit stochastic gradient descent quantization (OBSGD) is employed, achieving a convergence rate of O(1/√T). Additionally, VR users’ FoVs are grouped into multicast or unicast groups based on demand and wireless channel dynamics, optimizing network resource use. Evaluations on realistic VR head-tracking data demonstrate that the proposed DP-FL caching outperforms baseline methods in reducing average delay and improving cache hit rates, offering an efficient solution for wireless VR content delivery.

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