The vast amount of content generated in the Meta verse and unpredictable user demands make real-time optimization of communication, computing, and caching increasingly challenging. These issues highlight the need for intelligent mechanisms that reduce redundant content transmission and improve resource efficiency. To address this, joint semantic aware caching and rendering schemes that leverage content similarity are proposed to enable reusability across Metaverse environments. The goal is to optimize user-server associations, caching, and rendering decisions to efficiently utilize network resources, thereby maximizing resource savings and service quality. Reusing content across heterogeneous Metaverse environments, however, requires a learning algorithm capable of adapting to diverse task settings. To this end, a lifelong learning–based algorithm, Deep-Centralized ELLA (DC-ELLA), incorporating dictionary learning is developed to accommodate diverse user requests by dynamically extracting knowledge from different semantic environments. Simulation results show that the proposed caching and rendering schemes significantly outperform traditional approaches, while DC-ELLA enhances convergence speed and stability, demonstrating superior performance in dynamic scenarios. By exploiting knowledge and content from prior requests, the approach achieves scalable adaptation to new Metaverse environments.

As the Metaverse evolves with developments in AI, semantic communication, edge computing, and blockchain, it encounters challenges in adapting to dynamic environments and meeting rising communication and computation needs. In this article, we propose a semantic-aware UAV-based architecture tailored to the dynamic Metaverse that leverages UAV swarms consisting of a collection UAV and edge UAV servers. By mapping different semantic features of Metaverse environments, such as amount of tasks, arrival rate, throughput, and latency requirements, we jointly optimize the mobility, task allocation, and resource allocation in a dynamic Metaverse system. First, a particle swarm optimization-based collection-edge mobility algorithm (PSO-CEMA) is designed to optimize the mobility of UAV servers. Second, to facilitate timely and stable task allocation with reduced complexity, we propose a dual-queue system and a Lyapunov drift function-based dynamic programming task allocation algorithm (LDF-DPTAA). Then, we adopt lifelong learning and design a collection-edge joint training and processing algorithm (LL-CJTPA) to optimize the dynamic allocation of computational resources in the swarm. Finally, we integrate our algorithms into a PSO-LDF-LL algorithm to serve the dynamic Metaverse system. Simulation results show that our approach effectively optimizes UAV servers’ positions and task allocation, significantly reduces the training time when facing new tasks, and enhances the stability and efficiency of the network in dynamic settings while reducing congestion.