## 5 Variance Analysis  
We performed a statistical analysis (Variance Analysis) and generated the response surface using [43]. A significance level of 0.05 (Alpha (\(\alpha\)) = 0.05) was used for the statistical analysis of the data; we defined this value in order for the test to be rigorous in the statistical perspective. Table 4 shows the model obtained for each video. The rows present the coefficients associated with each term in the model, the factors exhibiting statistical influence and the calculated R-squared value for each model. These illustrate that the factor exhibiting statistical influence on the three videos is packet loss, which is one of the most critical factors when transmitting video. Table 4 also shows the constants associated with each term in the proposed models, including terms that exhibited a statistically low contribution to each model. Terms with a low contribution were not removed from the final model in order to retain the initially calculated R-squared value. This establishes that each model explained the variability of its data with a confidence level of close to 80% [44]. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation time & 100 s \\ \hline Simulation area & 600 \(\times\) 600 \(\times\) 200 \\ \hline Number of UAVs & 5 \\ \hline UAVs speed & 15 m/s \\ \hline Transmission range & 60 m \\ \hline Routing Protocol & AODV \\ \hline Transport Protocol & TCP \\ \hline Internet Protocol & IPv6 \\ \hline Video & highway \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation data for streaming video on FANET using a drone. We do not propose a single model to evaluate QoE, as other types of constants need to be introduced to the model. Such constants are necessary to maintain the model's R-squared value at a high level, and, in some cases, the calculated constants are based on the videos used to generate the data, or on the conditions of the user environment. With the models proposed in Table 4, it is not necessary to consider external variables (screen size, type of service, variables calculated with subjective tests etc. ), which means that the model can be applied online only by obtaining the values of the QoS parameters (delay, jitter and packet loss) from the video stream. The QoE value yielded by our proposed models is within the VQM scale and must, therefore, be converted to a MOS rank for convenient interpretation of the obtained results. The conversion is performed using the Video Quality Assessment Mapping (VQAMap) procedure discussed in [40], which makes it possible to map an arbitrary scale to a MOS rank. Figure 3 summarizes the methodology used for model generation. The graph on the left shows the inputs for our proposal; the QoS factors are shown on the x-axis, and the objective method to assess video QoE (VQM) on the y-axis. Thus, we decided to use RSM (Response Surface Methodology) together with CCD (Central Composite Design) to define the experiment to produce the data. RSM yielded a second-order model (Equation (1)), and CCD defined that \"N\" executions will be performed on each video. Each execution features a four-value vector: Delay, Jitter, Packet Loss and QoE value measured through VQM (QoE_VQM). These vectors were used to create the models (See Table 4, Equation (14)). The proposed models output QoE in the VQM scale; therefore, the VQM-to-MOS scale block maps the VQM value obtained from our models to its MOS equivalent using VQAMap.