# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 4 Experiments and Results
### QBR with Reservation Experimental Results  
To validate the proposed reservation methodology, an investigation has been performed to observe how reservation is affecting video QoE. The following simulations parameters helped to measure the performance of the suggested technique. This experiment used three video streams' traffic in MP4 format; VQoSRR classified them into an HD video stream, SD video stream, and best effort video stream. Both HD and SD have specific bandwidth and packet loss rate metrics. They transmitted and decoded simultaneously in real-time through the network; their QoE parameters specifications have been described previously in Table 3. Additionally, on each OVS switch port connected to the network, three queues have been added. One queue was reserved for each of the VQoSRR flow types. The maximum capacity supported on every link was 50 Mbps. To obtain the results, we conducted three test cases. The first case used the controller default setting without considering QoS and traded all videos as best effort (No_VQoSRR). The second case applied only the QoS-based routing proposed algorithm with the dynamic rerouting algorithm (VQoS-R). The third case combined the dynamic route computation with the per-class queue reservation scheme (VQoS-RR). Simulation parameters that were common for all the test cases are presented in Table 4.  
Figure 9: Effects of executing dynamic rerouting algorithm at different times on HD video resolution. (**a**) SSIM values are measured every 10 frames; (**b**) SSIM values are measured every 200 frames. Figure 10a,b illustrate the SSIM calculated in the test cases for SD and HD, respectively. In the figures, it can be seen that the proposed methods (VQoS-R and VQoS-RR) resulted in an improvement in the SSIM scores, and significantly, they were more efficient than the traditional controller shortest path method (No_VQoSRR) where the quality degradation was mostly observed. Furthermore, VQoS-RR offered a better quality video stream than VQoS-R. Moreover, Figure 11 shows the computed SSIM average values for the received videos streams. As observed from the graph, VQoS-R and No_VQoSRR yielded lower SSIM scores \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Video Type**} & \multirow{2}{*}{**No\_VQoSRR**} & \multicolumn{3}{c}{**VQoS-R**} & \multicolumn{3}{c}{**VQoS-RR**} \\ \cline{2-7}  & & **Loss Metric** & **Bandwidth Metric** & **Loss Metric** & **Bandwidth Metric** & **Queues Rates** \\ \hline
**Video 1, SD** & - & 0.5\% & 3 Mb/s & 0.5\% & 3 Mb/s & Estimated around 30\% \\ \hline
**Video 2, HD** & - & 0.05\% & 10 Mb/s & 0.05\% & 10 Mb/s & Estimated around 45\% \\ \hline
**Video 3, HD** & - & Best Effort & Best Effort & Best Effort & Best Effort & Estimated around 25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation parameters. Figure 10: (**a**) Influence of test cases on SD video resolution; (**b**) influence of test cases on the HD video. compared to using VQoS-RR. As we have seen, the VQoS-RR provided good average values for all transmitted videos, including the best effort videos. From the figure, we can observe that VQoS-RR improved video quality; for instance, in SD, the SSIM value measured in the case of No_VQoSRR was 0.91, and when measured in the case of VQoS-RR increased to 0.95. Accordingly, the SSIM value for HD was 0.92 for No_VQoSRR, whereas it increased to 0.96 for VQoS-RR. The reason is that the VQoS-RR method combined the reservation mechanism with QoS-based routing, which enhanced end-user perception of quality. Below are the results obtained from the subjective QoE tests, where Figures 12 and 13 represent the MOS scores rated by users of 15 test conditions for each test case; these measurements were based on MOS scores (1-5). Figure 12a,b present a comparison for SD and HD videos with the MOS scores, respectively.