## 5 Performance Evaluation
### Performance Evaluation of the Buffer-Based ABR Technique Using Predicted Network Bandwidth Values  
The QoE was assessed using the mean opinion score (MOS), which is a standard metric for measuring voice or video quality, to evaluate the performance of the proposed buffer-based ABR technique [19; 32]. The QoE is affected primarily by video quality, video quality change, and rebuffering. The best QoE is attained when the highest video quality is streamed without rebuffering or quality degradation. The worst QoE is obtained when the lowest video quality is streamed and frequent rebuffering occurs. On average, even for videos of medium quality, frequent video quality changes reduce the QoE by distracting users from their streaming. Therefore, as video quality improves, the QoE improves; however, as video quality changes and rebuffering increases, the QoE decreases. The QoE was calculated using Equation (3), which incorporates video quality, the amount of rebuffering, and the number of video quality changes, as these factors significantly impact user QoE. The maximum possible QoE value is 100. In Equation (3), VQ represents the video quality during streaming and is computed as the ratio of the average size of all chunks at the highest video quality over the average size of the selected chunks. Here, \(C_{s}\) is the chunk size of the selected video quality, and \(C_{\text{max}}\) is the chunk size at the highest video quality. VQ increases with increasing video quality as the average size of the selected chunks increases, and it decreases with decreasing video quality as the average size of the chunks decreases. QC denotes the video quality change value, which is calculated by dividing \(Q_{c}\) (the number of video quality changes) by \(N_{c}\) (the total number of chunks) and multiplying the results by \(\alpha\). RC represents the rebuffering change value, which is obtained by dividing \(R_{c}\) (the number of rebuffering occurrences) by \(N_{c}\) and multiplying the result by \(\beta\). Because changes in video quality and rebuffering negatively affect QoE, the QoE score is calculated by subtracting these two values from VQ. The parameters \(\alpha\) and \(\beta\) were set to 100 and 500, respectively, reflecting the greater negative impact of rebuffering on QoE compared with video quality changes. \[ \begin{split}\text{QoE}&=\text{VQ}-(\text{QC}+ \text{RC})\\ \text{VQ}&=\left(\frac{C_{s}}{C_{\text{max}}} \right)\times 100\\ \text{QC}&=\left(\frac{Q_{c}}{N_{c}}\right)\times \alpha\\ \text{RC}&=\left(\frac{R_{c}}{N_{c}}\right)\times \beta\end{split}  \tag{3}\]  
The performances of the proposed technique and existing BBA were compared. Evaluations were conducted using simulations and videos provided by PBS, an American public broadcasting company. The video consisted of 215 chunks, each encoded with seven different video qualities. The network environment was modeled based on the LTE dataset described in Section 4.1, which was used to predict network bandwidth in Section 5.1. First, we measured QoE while walking, and Figure 14 presents the MOS results for each pedestrian scenario. In this figure, blue represents the MOS of BBA, and orange represents  
Figure 13: Comparison of the measured and predicted network bandwidths in train scenario. the MOS of the proposed technique. The proposed technique achieved an average MOS of 60, whereas BBA achieved an average MOS of 56, indicating that the proposed technique offers approximately 7% better QoE than BBA. The standard deviation of the proposed method is approximately 13, and that of BBA is approximately 17, which means that the QoE of the proposed method is more stable. In the pedestrian scenarios, the change in network bandwidth is not significant, but instantaneous fluctuations in network bandwidth while walking occur because multiple users access the same network simultaneously. Figure 10 shows that pedestrian situations 1, 2, and 7 have a low RMSE, indicating that the bandwidth prediction is accurate.