Unmanned Ariel Vehicles (UAVs) face increasing challenges in obtaining sensory data and transferring them to the user even before the completion of their flight for time-critical processing. Traditionally bounded by only area coverage and battery capacity, UAVs now need to meet network QoS requirement when streaming data. The emergence of 5G Device-to-Device (D2D) Networks enables high speed network communication for UAVs to transfer data via D2D links during a flight. The planning of UAV flight paths is now subject to both battery capacity and network quality of service (QoS) constraints. In this paper, we focus on the path planning for UAVs, which stream data to a data receiver machine, under the constraints of full area coverage and network throughput. We present a mathematical model to formulate the issue as a combinatorial optimization problem that attempts to minimize the flight cost of multiple UAVs covering the entire area. We show that the problem is NP-hard, therefore propose a heuristic method to derive the number of UAVs and determine their flight paths. The solution is proved to be bound by $K\cdot OPT$. We conduct simulations to evaluate how the size of the area and the maximal flight distance of a UAV affect the number of UAVs needed, and how the D2D channel parameters affect the link throughputs.

无人机Ariel车辆（UAV）在获取感官数据并将其传输给用户之前，甚至在完成对飞行时间的关键处理之前，都面临着越来越多的挑战。传统上，UAV仅受区域覆盖范围和电池容量的限制，现在在流数据传输时，UAV需要满足网络QoS要求。5G设备到设备（D2D）网络的出现使无人机可以在飞行过程中通过D2D链路进行高速网络通信。无人机飞行路线的规划现在受制于电池容量和网络服务质量（QoS）约束。在本文中，我们专注于无人机的路径规划，该无人机在全覆盖范围和网络吞吐量的约束下将数据流传输到数据接收器。我们提出了一个数学模型来将该问题表述为组合优化问题，以试图最小化覆盖整个区域的多架无人机的飞行成本。我们表明问题是NP难的，因此提出了一种启发式方法来得出无人机的数量并确定其飞行路径。证明该解受约束$ķ\cdot ØPŤ$。我们进行仿真以评估无人机的区域大小和最大飞行距离如何影响所需的无人机数量以及D2D通道参数如何影响链路吞吐量。