QoE-aware Multicast-Unicast Transmission Scheme For Multi-UAV Assisted Virtual Reality Live Streaming Systems

The growing complexity of the sixth-generation (6G) wireless networks has positioned artificial intelligence (AI) as a critical tool for radio resource management (RRM). However, the opacity of AI learning models undermines their trustworthiness, robustness, and interpretability, significantly impeding their application in network resource management for 6G scenarios. Establishing reliable edge intelligence (EI) is crucial for the future of various domains, particularly in virtual reality (VR). By leveraging EI, VR applications can better meet user demands, enhancing service experiences and reliability. The 360-degree video characteristics challenge existing VR transmission networks. We propose a unmanned aerial vehicle (UAV)-assisted joint multicast-unicast system to optimize user QoE by jointly optimizing UAV trajectories, user associations, quality requests, user pairing, and power allocation. To address this optimization problem, we first decompose it into inter-frame and intra-frame components, and propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm to tackle the inter-frame problem. After solving the inter-frame problem, we utilize the quantum particle swarm optimization (QPSO) algorithm and matching theory to address the intra-frame problem. Simulation results demonstrate that our proposed algorithm, significantly improves the QoE for users.