Random network coding (RNC) is an efficient coding scheme to improve the performance of the broadband networks, especially for multimedia applications which are popular in 5G network. However, it is a challenging work to transmit the real time media data because of the time limitation and wide band requirement. Moreover, the topology of the network changes due to users' movement, causing huge channel heterogeneity in large wireless network area. In this case, the fixed macro base station (BS) or access point may not fit the real-time user distributions. Accordingly, the UAV-based BS with high mobility can provide flexible service by adjusting it position according to users' locations to fit the dynamic topology of the network. Therefore, in this paper, we propose a UAV-based adaptive RNC (UARNC) scheme that jointly optimizes the UAV's location and RNC packet scheduling to maximize the throughput in a multicast network while guaranteeing the service quality of the bottleneck users. This problem is formulated as an optimization problem, and the greedy scheduling techniques and particle swarm optimization (PSO) algorithm are adopted to solve it. Finally, the simulation results prove the effectiveness of the proposed scheme.

翻译：随机网络编码(RNC)是提高宽带网络性能的有效编码办法,特别是5G网络中流行的多媒体应用,然而,由于时间限制和宽幅要求,传输实时媒体数据是一项艰巨的工作;此外,由于用户的移动,造成大型无线网络区域频道差异巨大,网络的地形变化导致大型无线网络区域出现巨大的频道差异;在这种情况下,固定宏观基站(BS)或接入点可能不符合实时用户分布。因此,基于UAV的高流动性BS可以根据用户位置调整其位置,以适应网络的动态地形,从而提供灵活的服务。因此,在本文件中,我们提出了一个基于UAV的适应性RNC(UARNC)适应性计划(UARNC),以共同优化UAV的位置和RNC包的排列,以尽量扩大多播送网络的吞吐量,同时保证瓶端用户的服务质量。这一问题被表述为优化问题,而贪婪的调度技术和粒子优化(PSO)算法则被采纳,从而解决这一问题。最后,模拟结果证明了拟议的计划的有效性。