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Cache-Enabled XR Systems: Delay-Aware Resource Allocation for Immersive Experience
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DOI：10.1109/OJCOMS.2025.3639583.png)
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Abstract
En 中文
Extended Reality (XR) applications offer immersive experiences across industrial, healthcare, educational, and entertainment sectors, but they demand ultra-low latency and high data rates that challenge current cellular infrastructure. This paper proposes a latency-aware mobile XR system comprising multi-antenna base stations (BSs) and edge servers, each equipped with limited fronthaul capacity and local caching. To minimize end-to-end latency, we develop a unified optimization framework that jointly addresses field of view (FOV) caching and rendering, BS selection, beamforming vector design, and edge server placement. The framework captures the inter-dependencies between user-specific FOVs, rendering decisions, and resource constraints such as computation capacity and power, ultimately enhancing the quality of personal experience (QoPE). We formulate the problem as a mixed-integer non-convex program and solve it using $\ell _{0}$ -norm relaxation, successive convex approximation, and fractional programming. Reformulating it as a multiple choice multiple dimensional knapsack problem (MMKP), we apply Lagrangian dual decomposition to derive efficient solutions. Simulation results demonstrate that our approach significantly outperforms baseline algorithms. Notably, a 91% reduction in average delay is achieved when varying BS cache size, and a 94% improvement is observed over the greedy-edge method when adjusting edge server cache size. These results highlight the potential of the proposed method for scalable, delay-aware XR systems.
Keywords:
Extended reality
edge caching
server placement
low latency
quality of personal experience
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