MATE: A D2D-Enhanced Multi-Bitrate Video Caching Strategy for Cloud-Edge-Device Collaborative Networks

Edge caching alleviates backhaul pressure and enhances video service quality by deploying video content near user devices. However, the limited storage capacity of edge servers struggles to cope with the exponential growth of video data, challenging the delivery of high-quality video services. While both Device-to-Device (D2D) caching and multi-bitrate video technology are promising solutions to relieve the pressure on edge servers, existing research suffers from a key limitation: studies on multi-bitrate caching are predominantly focused on the edge layer, while D2D caching is often limited to single-bitrate scenarios. This isolation neglects the significant benefits of integrating D2D caching with multi-bitrate technology and fails to develop a cross-layer caching strategy for multi-bitrate videos. To address this limitation, we propose a D2D-enhanced Multi-bitrate video cAching straTEgy (MATE) for cloud-edge-device collaborative networks. We formulate a joint service latency and caching replacement cost optimization problem, which can be modeled as a mixed-integer programming problem. To overcome the coupling between caching strategies at the edge layer and device layer, we employ an alternating iterative optimization approach to decouple the original problem into two subproblems. We design an edge-device double-layer joint caching strategy, i.e., a device-layer caching strategy based on greedy algorithm and Lagrange multipliers, and an edge-layer caching strategy based on multi-agent twin delayed deep deterministic policy gradient algorithm. Extensive simulations are conducted to demonstrate the effectiveness of the proposed MATE.