Mobile Edge Computing (MEC) enables immersive XR with multimodal data by coordinating communication, computation, and caching (3C) resources upcoming 6G. The traditional communication constrained by Shannon’s theorem cannot accommodate the user demands for ultra-reliability and high throughput. In contrast, the semantic communication improves the quality of service and user experience by exploiting the semantic features. This paper constructs a multi-user MEC structure based on multimodal semantic communication for interactive AR/VR games. We construct a bidirectional caching task model to achieve cache-enhanced computing. To minimize the system cost, including the user latency, energy consumption, and storage size, we propose a content popularity-based DQN (CP-DQN) algorithm to make caching decisions. Then the CP-DQN is extended to the cache–computation coordination optimization algorithm (CCCA) to achieve the 3C resources tradeoff. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of caching hit ratio, computation cost and edge resource utilization.

移动边缘计算 (MEC) 通过协调即将到来的 6G 的通信、计算和缓存 (3C) 资源，实现具有多模态数据的沉浸式 XR。受香农定理约束的传统通信无法满足用户对超高可靠性和高吞吐量的需求。相比之下，语义通信通过利用语义特征来提高服务质量和用户体验。本文构建了一种基于多模态语义通信的交互式 AR/VR 游戏的多用户 MEC 结构。我们构建了一个双向缓存任务模型来实现缓存增强计算。为了最小化系统成本，包括用户延迟、能耗和存储大小，我们提出了一种基于内容流行度的 DQN (CP-DQN) 算法来做出缓存决策。然后将 CP-DQN 扩展到缓存计算协调优化算法 (CCCA) 以实现 3C 资源权衡。仿真结果表明，该算法在缓存命中率、计算成本和边缘资源利用率方面均优于现有算法。