# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 2 Related Works
### The Allocation Object  
The communication links are different in multi-UAV networks and they can be summarized into four categories: Air-to-Ground (A2G), Ground-to-Air (G2A), Air-to-Air (A2A), and Ground-to-Ground (G2G) [15]. Among them, A2G links may adopt IEEE 802.11 standard or other customized networks, which could provide high speed data transmission and large-scale coverage. It is also the typical data service link for video transmission. The inter-UAV links of A2A may use the low power consumption and less complicated standard such as the IEEE 802.14.4. The network topologies can be classified as star, multi-star, mesh and hierarchical mesh [16].  
In wireless communication systems, there are usually two kinds of channels: control channel and data channel. A typical UAV cluster communication topology is shown in Figure 1. Due to the relative motion between UAVs, multi-UAVs will constitute a fluid topology, which will lead to the instability of inter-UAV communication. Usually, if UAVs collect a large amount of data to transmit, such as high-resolution video, they mainly rely on A2G links and use the star network topology. The blue straight lines in the figure show the video data link, and the video captured by each UAV is sent directly to the base station.  
From the view of control channel, there are usually two kinds of ways for the control information transmission. One is achieved by the star topology, which relies on the central node to uniformly schedule resource allocation, as shown by the green dotted line in Figure 1. In these centralized control algorithms, the base station as the central node should know the parameters of all UAVs, based on which resources can be calculated in a collective way. This method can make full use of the computing advantages of the central node. It has certain advantages in some specific scenarios [17; 18; 19]. However, in star networks, central nodes need to predict the information of all of the UAVs. It is difficult to be realized in the application service oriented allocation. In addition, all of the information exchanges among UAVs would be through the base station and the occurrence of blockage of links and higher latency would bring adverse effects.  
The other way to realize the allocation control is through the distributed information interaction between UAVs in mesh topology, as shown in the red dotted line in Figure 1. Distributed algorithms have the characteristics of high efficiency, scalability and robustness, and have become the mainstream methods of resource allocation. It avoids the complex computation of the central node and, to some extent, saves the network resources. [PERSON]. [20] determined the distributed uplink transmit power of IoT nodes. Ref. [21] proposed a distributed power allocation algorithm based on an alternating direction method of multipliers in UAV-assisted networks. [PERSON]. [22] took game theory as a distributed way for resource allocation. However, the specific implementation of allocation is different from traditional mobile networks, such as the Ad Hoc Network. The multi-UAV formation is dynamic and the topology of the cluster is fluid. This may bring the possible intermittence of the A2A communication link and lead to the failure of control information transmission.  
In addition to the links and networking issues, the problems of flying altitude, trajectory and speed, completion time, energy consumption, etc. [23; 24] also need to be considered in multi-UAV communication. All of them may bring challenges to the resource allocation of the UAV cluster.