Quality of Experience Optimization for AR Service in an MEC Federation System

Abstract

Augmented reality (AR) in the internet of things requires ultra-low latency, high-resolution video, and fairness in multi-user environments, which pose challenges for traditional cloud and edge computing. To address this shortcoming, we studied AR subtask offloading and resource allocation in a multi-hop, multi-access edge computing federation. Our approach improves the quality of experience (QoE) by optimizing video quality and reducing delay while ensuring fairness, which is modeled as the ratio between provided and required quality. Instead of sequential execution, we adopt parallel AR subtask dependency processing to minimize latency. We propose an improved deep deterministic policy gradient algorithm for efficient solution exploration. Additionally, we implement strict training process monitoring to optimize resource usage and ensure sustainability. Experiments demonstrate that our method improves QoE by nearly 8% compared with TD3 while cutting training time in half.