# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 6 Simulation Results
### Dataset 2  
Here, we analyze the performance of the algorithms when streaming the Big Buck Bunny video (Dataset 2). In the first experiment, the buffer size and segment duration were set to 15 s and 2 s, respectively. In the next segment, buffer size and segment duration were increased to 60 s and 4 s, respectively. Table 7 shows the performance of the algorithms when the buffer size and segment duration were set to 15 s and 2 s, respectively. Like the previous experiments, the proposed algorithm achieved the highest video rate and had the lowest bandwidth inefficiency. The proposed algorithm was able to avoid rebuffering events and achieved the best user experience. The QLSA and DASH-Google algorithms achieved a high video rate but also experienced a high rate-switching ratio. The DBT algorithm experienced few video rate switches and avoided buffer underflow. However, it had the lowest video rate among the competing algorithms. Similar to the experiments conducted with Dataset 1, the FDASH algorithm experienced the fewest video rate switches but at the expense of more playback interruptions. Figure 10 shows that only the FDASH algorithm experienced playback interruptions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Adaptation Algorithms** & **Proposed** & **FDASH** & **DBT** & **QLSA** & **DASH-Google** \\ \hline Average Video Rate (kbps) & 1274.64 & 1236.42 & 1055.77 & 1080.90 & 1225.20 \\ Switching Ratio & 0.26 & 0.1 & 0.05 & 0.32 & 0.47 \\ Inefficiency & 0.20 & 0.251 & 0.32 & 0.32 & 0.15 \\ Average of Switches & & 664.76 & 5986.98 & 834.45 & 572.24 & 639.08 \\ (kbpps) & & 1092.89 & 986.27 & 981.41 & 849.02 & 949.86 \\ QoE & 3.39 & 3.02 & 3.14 & 2.99 & 3.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of algorithms at a buffer size of 60 s and a segment duration of 4 s.  
Figure 9: Comparison of (**a**) rebufferings per client, and average number of interruptions, and (**b**) average buffering time when the clients downloaded Dataset 1. The buffer size and segment duration were set to 60 s and 4 s, respectively. When the buffer size and segment duration were set to 15 s and 2 s, respectively, a comparison of Tables 4 and 7 indicates that the DBT algorithm achieved the highest QoE and MOS values when downloading Dataset 1, but it achieved the lowest QoE value and the second lowest MOS value when streaming Dataset 2. Similarly, Table 4 shows that the QLSA algorithm achieved the second lowest QoE and MOS values among the competing clients while downloading Dataset 1, whereas it achieved the second highest QoE and MOS values when streaming Dataset 2. As explained in Section 1, the existing algorithms employ fixed control rules to pick the video rate. This results in inconsistent performance from the algorithms as the client settings and video content characteristics change. On the other hand, the proposed algorithm achieved a high QoE, irrespective of client settings or dataset characteristics. Next, we increased the buffer size and segment duration to 60 s and 4 s. Table 8 shows that the proposed and QLSA algorithms achieved high video rates. Both algorithms were able to avoid playback interruptions. The proposed algorithm achieved a slightly lower switching ratio, compared to QLSA, which resulted in slightly better user experience. The DBT algorithm achieved the fewest video rate switches at the expense of low video quality. FDASH and DASH-Google achieved similar video rates. However, a high frequency of video rate switches led to low user experience from DASH-Google. Figure 10 shows that only the FDASH algorithm experienced playback interruptions. A comparison of Figures 10 and 11 shows that the number of buffering events and the buffering time decreased with an increase in buffer size.