# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 3 System Model and Utility Function  
### System Model  
The development of various wireless technologies has greatly expanded the application of UAVs. For example, the Massive Multiple-Input Multiple-Output (MIMO) technology used in 5G network breaks the limitations of traditional 2D-MIMO and increases the vertical dimension, which is more suitable for the three-dimensional nature of UAVs. At present, people have completed the UAV flight communication tests based on the 5G network for many times and achieved High-Definition (HD) live videos' broadcasting. Not only 5G cellular networks, but also many other wireless technologiescould provide UAVs with favorable communication environments [38]. Without loss of generality, in this paper, we suppose several UAVs, as a cluster, are flying in the area covered by a customized network. They perform the tasks of acquiring and uploading videos. Figure 2 shows a typical scenario that needs to be discussed in this paper. There are N UAVs in the network. Suppose they are all in the same coverage area of a wireless Access Point (AP), which belongs to a certain Network Service Provider (NSP).  
Each UAV is equipped with video sensors and communication payload for filming and sending the encoded video back via the wireless network. In this process, the flying position and speed of the UAV may be different, and the shooting angle and object may also be different, which makes them get different videos. From the perspective of intra-frame difference, if the background of the image is messy or the targets are numerous, then such video is relatively complex in terms of content. From the perspective of inter-frame difference, if the motion rate of subjects is fast or the change range is large, the video content will be more complex. Video scene fragments collected by different UAVs in the cluster are compressed, encoded and finally sent to the UAV Control and Data Center (UCDC). Besides controlling the flight speed and trajectory of the UAV cluster, the UCDC also integrates and edits the encoded fragments obtained by the UAVs, which are eventually used for various commercial purposes. Meanwhile, UCDC needs to lease wireless channels from the NSP for each UAV. Flight control information usually consumes very few resources, so most of the rental fee is related to the actual channel bandwidth occupied by each video stream. It is hoped that UCDC can obtain high-quality video signals, so that they can meet the users' needs in QoE finally. When the wireless resource is insufficient, the unfairness of uplink channel allocation will lead to severe transmission error or packet loss of some individuals and eventually affect the total watching experience of all gathered videos. Therefore, the limited bandwidth resources need to be reasonably allocated.  
Suppose that _Cband_ represents the total throughput that the AP can provide. Let \(r_{i}\in\textit{Cband}\) represent the channel bandwidth occupied by the video of the \(i\)-th UAV, which varies from the minimum rate constraint \(R_{i}^{\min}\) to the maximum one \(R_{i}^{\max}\), \(i=1,2,...,N\). \(R\)\(=\)\([r_{1},r_{2},...,r_{N}]\) represents the transmission rate vector of N videos, and \(U\)\(=\)\([u_{1},u_{2},...,u_{N}]\) represents the corresponding utility function vector. In order to return high-quality video, each UAV intends to magnify their utilities:  
Figure 2: Video uploading of a UAV cluster via a wireless network.  
\[\begin{split}\max& u_{i,}\\ s.t.& R_{i}^{\min}\leq r_{i}\leq R_{i}^{\max},\\ & 0\leq\sum_{i=1}^{N}r_{i}\leq Cband.\end{split} \tag{1}\]