## 4 Performance Evaluation
### Results and Discussion
#### 4.2.2 QoE Measurements  
Video streaming is one of the defiant issues in VANETs due to their exception topology and connectivity disruptions. Our contribution aims to improve the quality of experience (QoE) of the received video. Thus, QoE metrics, as a function of network density, are crucial metrics to consider when evaluating the performance of different routing protocols [36; 49].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{4}{c}{**QoS Metrics**} \\ \cline{2-5}  & **PDR** & **Delay** & **Throughput** & **Overload** \\ \hline
20 & \(\uparrow\)24.50\% & \(\uparrow\)32.04\% & \(\uparrow\)23.99\% & \(\downarrow\)33.33\% \\
50 & \(\uparrow\)17.72\% & \(\uparrow\)39.42\% & \(\uparrow\)22.59\% & \(\downarrow\)42.11\% \\
100 & \(\uparrow\)13.89\% & \(\uparrow\)44.41\% & \(\uparrow\)17.03\% & \(\downarrow\)21.73\% \\
150 & \(\uparrow\)15.26\% & \(\uparrow\)25.96\% & \(\uparrow\)47.07\% & \(\downarrow\)25.71\% \\
200 & \(\uparrow\)23.28\% & \(\uparrow\)22.04\% & \(\uparrow\)39.19\% & \(\downarrow\)17.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: MNH-FGR’s QoS improvement rate regarding GPSR-kP for all different density scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Routing**} & \multicolumn{4}{c}{**QoS Metrics**} \\ \cline{2-5}  & **PDR** & **Delay** & **Throughput** & **Overload** \\ \hline GPSR-2P & \(\uparrow\)47.35\% & \(\uparrow\)10.65\% & \(\uparrow\)73.26\% & \(\downarrow\)61.7\% \\ GPSR-kP & \(\uparrow\)26.61\% & \(\uparrow\)44.41\% & \(\uparrow\)47.07\% & \(\downarrow\)42.11\% \\ FzGR & \(\uparrow\)9.63\% & \(\downarrow\)13.64\% & \(\uparrow\)35.55\% & \(\downarrow\)11.76\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: MNH-FGR’s maximal QoS improvement rate regarding FzGR, GPSR-2P, and GPSR-kP.  
Figure 7 compares the video quality of _Foreman_, perceived by the user, for each used routing protocol, including FzGR, GPSR-2P, GPSR-KP, and MNH-FGR. We notice that MNH-FGR provided better values compared with other protocols. Thus, as summarized in Table 4, MNH-FGR increased the PSNR and SSIM parameters, respectively, up to 25.30% and 29.62% compared with GPSRJP. We also notice that the VQM parameter was reduced by 42.88%.  
In addition, it is especially necessary to mark the improvement rate of our new variant compared with our first contribution. Indeed, according to Table 5, the MNH-FGR protocol can increase the signal noise rate, as well as the structural similarity index, respectively, up to 12.5% and 23.81% compared with FzGR. The video quality metric, meanwhile, is reduced by 31.03%.  
Figure 7: QoE measurements as a function of density: (**a**) VQM, (**b**) PSNR, and (**c**) SSIM.  
In short, the results presented in this last part about QoE demonstrate the superiority of the MNH-FGR routing protocol over the other protocols tested in terms of improving the video quality perceived by users. The improvement rate of MNH-FGR compared with FzGR, GPSR-2P, and GPSR-kP was significant in all scenarios tested. These findings are particularly relevant for researchers and engineers in the field of wireless sensor networks who want to optimize the performance of their routing protocols and ensure high QoE for users. The MNH-FGR protocol provides a promising solution for improving the QoE of multimedia applications in WSNs, and further research can focus on exploring its potential in more complex scenarios.