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Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. In this paper, we integrate both the networking techniques and cloud computing tasks of multi-UAV systems. We commence by proposing an energy efficient scheme for selecting the gateway of UAVs invoked for relaying data to the heterogenous cloud. Then, relying on queuing theory and Lyapunov optimization, we strike a powerdelay trade-off by jointly optimizing the computational task scheduling and resource allocation in the heterogeneous cloud architecture, which is comprised of an edge cloud and a powerful remote cloud. We analyze the optimal resource-allocation strategy for each time slot and an iterative algorithm is conceived for reducing the computational complexity. Finally, our numerical results demonstrate the superiority of the proposed scheme.

With growing human maritime activities, supporting low-cost and high-speed information services for users at sea has become imperative. However, traditional means of maritime communications fail to provide high rate services due to their high cost and limited bandwidth. In this paper, considering a base station ashore and several offshore relay nodes, we propose a cooperative multicast communication scheme for maritime users relying on joint beamforming (BF) optimization and relay design. Specifically, we decompose our proposed joint optimization problem into two subproblems, which can be solved by the feasible point pursuit successive convex approximation approach. Furthermore, an alternating optimization algorithm is proposed, which imposes an exponentially increasing complexity as a function of the number of BF elements and the number of relays, when aiming for finding the globally optimal solution. In order to reduce this excessive computational complexity, a low-complexity distributed algorithm is also conceived and its closed-form solution is derived. Finally, the simulation results provided show that our proposed algorithm is beneficial in terms of increasing both the throughput as well as the energy efficiency.