Extended reality (XR) applications often perform resource-intensive tasks, which are computed remotely, a process that prioritizes the latency criticality aspect. To this end, this paper shows that through leveraging the power of the central cloud (CC), the close proximity of edge computers (ECs), and the flexibility of uncrewed aerial vehicles (UAVs), a UAV-aided hybrid cloud/mobile-edge computing architecture promises to handle the intricate requirements of future XR applications. In this context, this paper distinguishes between two types of XR devices, namely, strong and weak devices. The paper then introduces a cooperative non-orthogonal multiple access (Co-NOMA) scheme, pairing strong and weak devices, so as to aid the XR devices quality-of-user experience by intelligently selecting either the direct or the relay links toward the weak XR devices. A sum logarithmic-rate maximization problem is, thus, formulated so as to jointly determine the computation and communication resources, and link-selection strategy as a means to strike a trade-off between the system throughput and fairness. Subject to realistic network constraints, e.g., power consumption and delay, the optimization problem is then solved iteratively via discrete relaxations, successive-convex approximation, and fractional programming, an approach which can be implemented in a distributed fashion across the network. Simulation results validate the proposed algorithms performance in terms of log-rate maximization, delay-sensitivity, scalability, and runtime performance. The practical distributed Co-NOMA implementation is particularly shown to offer appreciable benefits over traditional multiple access and NOMA methods, highlighting its applicability in decentralized XR systems.

翻译：扩展现实（XR）应用通常执行资源密集型任务，这些任务需远程计算，而该过程首先需满足时延关键性要求。为此，本文表明：通过利用中心云（CC）的强大算力、边缘计算机（EC）的近端优势以及无人飞行器（UAV）的灵活性，UAV辅助的混合云/移动边缘计算架构有望应对未来XR应用的复杂需求。在此背景下，本文区分了两种类型的XR设备（强设备与弱设备），并提出一种协作非正交多址接入（Co-NOMA）方案，通过配对强设备与弱设备，智能选择面向弱XR设备的直连链路或中继链路，以提升XR设备的用户体验质量。进而构建一个总对数速率最大化问题，联合确定计算与通信资源分配及链路选择策略，旨在实现系统吞吐量与公平性之间的权衡。在满足功耗、时延等实际网络约束条件下，该优化问题通过离散松弛、逐次凸近似和分数规划进行迭代求解且可分布式部署于网络各节点。仿真结果验证了所提算法在对数速率最大化、时延敏感性、可扩展性及运行性能方面的表现。特别地，相比传统多址接入与NOMA方法，实用的分布式Co-NOMA实现方案展现出显著优势，凸显其在去中心化XR系统中的适用性。