  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.
