A QoE-Oriented Scheduling Scheme for Energy-Efficient Computation Offloading in UAV Cloud System

Air ground integrated mobile cloud computing (MCC) provides UAVs with more flexibility and resilience from the cloud computing architecture. However, the increasing aerial mobile data requires heterogeneous quality of experience (QoE) for aerial accessing network. In addition, for the persistent flying, energy efficiency during the computation offloading should also be under consideration. This paper proposes an energy-efficient resource allocation scheme with the ability of QoE enhancement. Various aerial offloading data with different QoE requirements is stored and relayed in the multi-queueing architecture. Hence offloading rate differentiation is utilized to ensure the high-priority data a better QoE. The satisfaction function is designed with respect to energy efficiency and actual performance experienced by UAV. By using the Lyapunov optimization technique, the problem can be decoupled into two independent sub-problems. The first one is rate control associated with multi-queueing architecture in ground base-station (GBS) that manages the aerial offloading data from the UAVs according to the queue state information. The second one is resource allocation associated with the strategy of subcarrier assignment and power allocation according to the channel state information. The experiments demonstrate the algorithm has great properties such as maximization of the UAVs' satisfaction, the reliable heterogeneous QoE support and enhancement of the UAVs' transmission energy efficiency.