## 4 Performance Evaluation
As a result, we can infer that the inclusion of the secondary agent led to increase performance improvement and reduced penalties. This collaborative interaction between the two agents proved to be very effective. During the experiment, a distinct trend emerged: there was a significant decline in the number of alert messages transmitted by the overall system after approximately 55% of the video sequences had been incorporated into the training process. This decline indicates that the collaboration between the main agent and the secondary agent had positively influenced the decision-making process. The end result was  
Figure 6: Degree of performance improvement with 70% of video sequences dataset. Figure 7: Degree of performance improvement over time. a marked improvement in overall system performance, coupled with a reduced frequency of penalties or erroneous decisions. Figure 9 illustrates the results of a comprehensive assessment of various QoE metrics, including rebuffering events, average segment quality, and the overall QoE value. These metrics are pivotal indicators for evaluating the performance of our proposed approach. This experiment was conducted using the remaining 30% of video sequences and Network Norway Datasets. As a result, it can be confirmed that the number of rebuffering events decreases linearly according to experiments. Also, we observed that there were no rebuffering events after five experiments. The average segment quality and QoE value are high across the experimental timeline. From the experimental results, it can be concurrent that the proposed approach has improved the overall QoE values using the video sequences dataset with a very wide range of wireless network conditions. QoE assessment can be conducted through both subjective and objective methodologies. Subjective methods entail the collection of evaluations from individuals who assess  
Figure 8: Degree of performance improvement with a secondary agent. Figure 9: Results of QoE metrics. the service based on their personal satisfaction. In our endeavor to appraise QoE, we have embraced the mean opinion score (MOS) [45] as subjective evaluation method. In this experiment, we invited 20 users to actively participate in the testing phase. Each user played back video sequences from a designated video dataset. Figure 10 shows that the proposed solution achieved an impressive average MOS score of 4.5, which fall within the \"good\" range. This average MOS score signifies a high level of user satisfaction with the video streaming experience. The positive reception of our approach is a testament to its effectiveness in enhancing QoE for users. In this experiment, we conducted a comprehensive comparison of our proposed approach with several existing schemes: Pensieve [17], the DASH framework with online reinforcement learning [38], LBC [24], and Rainbow [29]. Based on the comparative results, it is clear that our proposed approach substantially improves the QoE, with improvements exceeding 96% compared to the existing schemes. Moreover, the evaluation of QoE metrics showed that our approach significantly reduces average rebuffering time below 1.8 s and minimizes average quality switching to less than 0.21%. Furthermore, the results obtained from the subjective MOS method validate the effectiveness of our approach and highlight its potential to enhance the overall streaming experience. This collective evidence solidifies the superiority of our approach in delivering an enhanced user experience. Figure 11 illustrates that our approach has the highest values in terms of normalized QoE value, average normalized QoE value, average switching quality, and average rebuffering time compared to the existing schemes, particularly, in low and fluctuating bandwidth conditions, the proposed approach and existing schemes showed high performance, but in medium and low bandwidth conditions, the proposed approach provided high and stable QoE.