Dual Time-Scale Resource Allocation for Hybrid VR and Haptic Services With Diffusion-Based DRL

Emerging immersive services, such as virtual reality (VR), are featured by multi-modal data streams (audio, video, and haptic). The fusion of distinct service characteristics introduces a significant challenge to wireless communications. This paper considers hybrid VR video and haptic services, where VR video segments and haptic data packets are transmitted in two different time scales. To optimize resource utilization, we employ dynamic time division duplexing (TDD) to address the asymmetry in uplink/downlink (UL/DL) traffic. We formulate a dual time-scale optimization problem that minimizes overall bandwidth while satisfying both the round-trip delay of VR service and the reliability and latency requirements of haptic service. To address this problem, we propose a hierarchical deep reinforcement learning (DRL) framework: 1) deep deterministic policy gradient (DDPG) optimizes total bandwidth at the beginning of each video segment’s transmission; 2) a diffusion-based actor-critic (Diffusion-AC) algorithm determines the UL time resource ratio at each time slot, which is significantly shorter than the transmission duration of a video segment. Simulation results show that the proposed algorithm can reduce the overall bandwidth usage by 20% compared with baseline methods. In addition, it provides greater adaptability across diverse scenarios and converges faster than the baseline methods.