Mobile edge computing (MEC) provides computational services at the edge of networks by offloading tasks from user equipments (UEs). This letter employs an unmanned aerial vehicle (UAV) as the edge computing server to execute offloaded tasks from the ground UEs. We jointly optimize user association, UAV trajectory, and uploading power of each UE to maximize sum bits offloaded from all UEs to the UAV, subject to energy constraint of the UAV and quality of service (QoS) of each UE. To address the non-convex optimization problem, we first decompose it into three subproblems that are solved with integer programming and successive convex optimization methods respectively. Then, we tackle the overall problem by the multi-variable iterative optimization algorithm. Simulations show that the proposed algorithm can achieve a better performance than other baseline schemes.

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Evidence indicates that requesting video clips on demand accounts for a dramatic increase in data traffic over cellular networks. Caching part of popular videos in the storage of small-cell base stations (SBS) in cellular networks is an efficient method to reduce transmission latency and mitigate redundant transmissions. In this paper, we propose a commercial caching system consisting of a video retailer (VR) and multiple network service providers (NSPs). Each NSP leases its SBSs, with some price, to the VR for the purpose of making profits, and the VR, after storing popular videos in the rented SBSs, can provide better local video services to the mobile users, thereby gaining more profits. We conceive this system within the framework of a Stackelberg game by treating the SBSs as a specific type of resources. Then, we establish the profit models for both the NSPs and the VR based on stochastic geometry. We further investigate the Stackelberg equilibrium by solving the optimization problems in two cases, i.e., whether or not the VR has a budget plan on renting the SBSs. Numerical results are provided for quantifying the proposed framework by showing its efficiency on pricing and resource allocation.

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