## 6 Results and Discussions
### The Impact of Low Traffic Load on Client Satisfaction  
This part shows the impact of low traffic load on the end-users' perceived QoE under three different topology scales. It presents the performance comparison of the proposed RL-based solution with the OSPF protocol in terms of the customers' satisfaction represented by SSIM and VMAF, network throughput and packet loss rate and delay variation for the generated traffic.  
SSIM and VMAF correlate well with the customer perception and allow an efficient calculation reflecting the clients' QoE. It can be seen from Figure 4 the obtained SSIM values for DASH video with the proposed RL-based solution maintained a high score in all the three topologies, representing almost an excellent viewing behaviour. The average SSIM values produced with the proposed scheme reaches 98% under the three networks. In the case of the OSPF protocol, QoE was reduced to the range between good and fair for the middle and large scale networks. However, with Abiene, the OSPF-based approach tends to have a slightly similar viewing experience to the RL-based solution. In addition, Figure 5 illustrates the average VMAF scores for DASH video with both schemes. It can be observed that RL-based solution improved the user-perceived quality, specifically under Geant and Cernet networks, and presents high scores (an average of 93 under all networks) leading to an excellent viewing experience.  
Table 3 shows that the RL-based solution produced a lower packet loss rate for the background traffic compared to the OSPF-based approach. With respect to the OSPF protocol, UDP traffic experiences higher data loss as the topology changes the scale from a small to a large network. For instance, the generated traffic between H7-H35 introduced a 0.10% loss rate in the OSPF-based approach since 128 packets were dropped out of 127,551. In comparison, the RL-based solution decreased the loss rate as only 28 packets were dropped out of 127,551, leading to a 0.022% loss rate. It can be observed that the improvement of packet loss rate with the RL-based solution occurs in all of the client-server applied in the experiment under various topology scales. However, The RL-based solution may impose delay since complexity lies in the dynamic selection of the optimal path for video transmission introduced by the RL agent, which forces the UDP traffic to take another route. In this experiment, the average packet jitter between client and server is monitored with our solution compared to the OSPF-based approach. As expected, it can be clearly noticed that the OSPF-based scheme presented less jitter in almost all of the client-server routes under various network topologies.  
Figure 4: SSIM values for DASH video under low traffic loads in three different topologies. (**a**) Abilene; (**b**) Geant; (**c**) Cernet.  
Figure 5: VMAF values for DASH video under low traffic loads in three different topologies.  
Figure 6 demonstrates the throughput of DASH video streaming when the network encounters low traffic loads. As noticed, the throughput drops with the OSPF-based scheme due to the increase in the packet loss rate. Although, in all the three topologies, both approaches present almost a similar performance, between 0-50 s. However, as the video streaming transmission continues, with the low background traffic running on the network, the throughput drops and remains stable at 2.8 Mbps for the OSPF-based approach. At this point, the customer faces re-buffering events because of throughput decrease situations that reduce the client QoE. Contrariwise, the results reveal a higher throughput with the proposed RL-based solution and the stability achieved at more than 3.5 Mbps in all the three topologies. The RL agent repeatedly interacts with the SDN _Infrastructure Plane_, avoids paths with a high data loss rate, and prioritises paths with large available bandwidth, enhancing network throughput and providing a better video quality.