# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 1 Introduction  
The great progress in aviation, new energy and artificial intelligence (AI) technologies has led to the rapid development of unmanned aerial vehicles (UAVs). They are becoming much smaller, lighter and more intelligent and are widely used in both civilian and military fields. UAVs can acquire images and videos in real time by the video sensors they carry. Meanwhile, they compress and encode the multimedia, and finally upload them through the wireless network. This way of real-time shooting and transmission makes UAVs play a great role in civil fields, such as land mapping, pollution monitoring, disaster management, and personal aerial photography [1; 2; 3]. In terms of military applications, UAVs have penetrated into all aspects of combat. The panoramic real-time video brought by military UAVs overturns the traditional close-in reconnaissance, situational awareness, fire evaluation and other military operation modes. The clusters of multi-UAVs, working cooperatively, are of great significance for the army to obtain battlefield information right. Although UAV videos have brought great convenience, they are a kind of information that may occupy a lot of resources, and the transmission capacity of wireless channel is limited. Currentvideo compression algorithms have been developed to be quite mature, such as H.264/AVC and H.265/HEVC, which can remove the redundancy in video signals more effectively. However, as the video resolution has been greatly improved, the requirement of wireless resource will also drastically increase. The coverage, information acquisition ability and destruction resistance of a single UAV is relatively limited. These problems can be solved well in the UAV cluster. A cluster of UAVs can present information on a much larger scale and from more angles [4]. However, in addition to the convenience, the UAV cluster has many other issues which need to be further considered, such as the communication links, the networking mode, routing mode, etc. Especially when their flying area is relatively concentrated, the shared wireless resources over a small area will not be infinite, which means they have to compete with each other. As a result, the effective allocation of resources will become very necessary. The insufficiency of channel resource may deteriorate the quality of video transmission, so we mainly consider the situation of limited channel bandwidth in the paper. The question of how to measure the deterioration, or from another aspect, how to measure the utilities, should be answered first. We need an objective metric. As an application layer service, the ultimate goal of transmitting video is to provide users with better viewing experience. Therefore, Quality of Experience (QoE) is a better satisfaction metric. The total QoE of all the videos could provide a clear indicator to evaluate the allocation effects. In this situation, the transmission rate is the main factor that could influence the QoE value. Therefore, when multi-UAVs in a cluster separately uplink videos through a wireless access point at the same time, how to allocate the limited bandwidth resource among them in order to maximize the total QoE has become an urgent and complex problem. The main contributions of this paper are summarized as follows:  
* We study the issue of rate allocation when multi-UAVs capture videos and send them back via the wireless channel simultaneously. The total QoE of all videos are considered as the optimization goal and the costs for channel renting and energy consumption have been deducted. * Based on the potential game, we build a new distributed resource allocation framework. According to the potential function we propose, the game is proved as a complete potential game and the correlated equilibrium of the game exists and is unique. * In order to make all the UAVs in the cluster iteratively update their bandwidth strategy, we adopt a distributed self-learning algorithm, by which the correlated equilibrium could be achieved with a relatively fast convergence rate.