# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 6 Simulation Results and Analyses
### Initial Analysis  
When the same video is compressed and encoded in different rates, it will bring users different viewing experiences. In order to validate this effect, we first use FFmpeg codec to encode the CIF video Coastguard into H.265 files. The original video's format is yuv420p, 352 \(\times\) 288, and the frame rate is 30 fps. After the encoding, at the time 1 s, 1.5 s and 2 s, the compressed video frames are extracted for comparison, and the results are shown in Figure 3. The encoding rate \(r_{i}\) is selected within the range shown in Table 1 and the corresponding Quantizer Parameter (QP) is also listed. When the encoding rate is low, the image is relatively fuzzy, the block effect is very obvious, and the experience quality is extremely bad. As the speed increases, the clarity is improved. When the rate is greater than 200 kbps, the effect of video is more easily accepted by users. Thus, if the UAV is assigned to a different bandwidth, the onboard encoder will update the encoding rate in order to achieve the new bandwidth, which will directly affect the video's QoE. In the following simulation, we believe that each UAV's encoder can achieve such adaptive video encoding. That means the rate variation of the encoder can synchronize with rate allocation. After the updating, the constant encoding rate equals to the newly specified bandwidth value in each iteration. We keep on observing the total utility changes brought by the new allocation, decide whether to change the rate, and further select the best allocation method according to the distributed self-learning algorithm. The simulation is based on Matlab R2017a platform. Ref. [38] gave some QoS parameters of several typical wireless technologies which can be used in UAV communication. In addition, the data rates could vary from 50 kbps to 10 Gbps. Various wireless networks can provide different channel bandwidth. In order to make the simulation analysis more universal and convincing, we set the total channel bandwidth of the system according to the requirements of video transmission. We sum the minimum and maximum rates of seven videos in Table 1 and the results are \(\sum\limits_{i=1}^{7}R_{i}^{\text{min}}=622\) kbps and \(\sum\limits_{i=1}^{7}R_{i}^{\text{max}}=5.485\) Mbps. That means if the total bandwidth is less than 622 kbps, it cannot meet the minimum requirements and the transmission of one or more videos will fail. When the total bandwidth is more than 5.485 Mbps, it can always meet the maximum requirements of each video and each video could be sent at its maximum rates. Thus, resource allocation is no longer necessary in this condition. Therefore, we choose to change the total transmission bandwidth from 1 Mbps to 6 Mbps in the simulation. In other words, a certain value within this range can be assigned as _Chand_ to verify the effect of the model and the algorithm we propose. In this way, the minimum transmission requirement of the seven videos can be guaranteed, but the maximum demand of each video cannot be met at the same time. Each UAV needs to acquire resources through competition. Therefore, the algorithm studied in this paper is mainly aimed at the situation of relatively insufficient resources. The users' needs and the system's ability of providing resources are always relative. Once the network cannot meet the highest requirements of all users, which leads them to compete for resources, we may adopt the allocation mechanism mentioned in this paper.