# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 6 Simulation Results
### Dataset 1  
Here, we analyze the performance of the proposed algorithm when streaming video segments from Dataset 1. As shown in Table 3, 10 clients simultaneously competed for the bandwidth. 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 30 s and 4 s, respectively. In the final experiment, buffer size was increased to 60 s.  
Table 4 displays the performance of the algorithms when the buffer size was set to 15 s and the segment duration was 2 s. Because the buffer size was small, the risk of playback interruption was high. In case of large fluctuations in the throughput, aggressively selecting the video rate could lead to playback interruption. Table 4 shows that the proposed \begin{table}
\begin{tabular}{l l} \hline Cell layout & Single hexagonal cell \\ UE distribution & Uniform \\ Path loss model & Hata Model PCS Extension \\ BS transmission power & 38 dBm \\ UE distance & 1–500 m \\ Scheduler & Proportional fairness \\ Cell layout & Single hexagonal cell \\ UE distribution & Uniform \\ \hline \end{tabular}
\end{table} 
Table 2: Cellular network configuration. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Dataset** & **Buffer Size** & **Segment Duration** & **Number of Clients** \\ \hline
1 & 1 & 15 s & 2 s & 10 \\
2 & 1 & 30 s & 4 s & 10 \\
3 & 1 & 60 s & 4 s & 10 \\
4 & 2 & 15 s & 2 s & 10 \\
5 & 2 & 60 s & 4 s & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiment settings used to analyze the performance of the algorithms. Figure 6: Network topology. algorithm selected the highest video rate from among the competing clients by efficiently utilizing the video rate. However, the proposed algorithm experienced a slightly high number of video rate switches. The reason behind the greater number of video rate switches is that when the buffer level increases, the proposed algorithm quickly increases the video rate by efficiently utilizing the bandwidth. Similarly, when the buffer level drops, the proposed algorithm reacts quickly to decrease the video rate to mitigate playback interruptions. Because the buffer level was small, and the throughput fluctuated as a mobile user moved at vehicular speed, this led to the algorithm downloading high-quality segments at the expense of slightly more video rate switches. Figure 7 shows that the proposed algorithm avoided unnecessary video playback interruptions while downloading high-quality segments. The rebufferings-per-client metric represents the ratio of clients that experienced a playback interruption to the total number of clients, while average interruptions is the number of times a client experienced a playback interruption. Table 4 shows that the DBT algorithm achieved a lower video rate than the proposed algorithm; however, the DBT algorithm had slightly higher QoE and MOS values, compared to the proposed algorithm, because it experienced fewer video rate switches. The DBT algorithm avoided switching video rates unless the buffer level increased above or decreased below predefined thresholds, irrespective of fluctuations in bandwidth. This approach helps mitigate unnecessary video rate switches. FDASH is a fuzzy-based algorithm that employs fuzzy logic to select the video rate and control the playback buffer level. In order to minimize video rate switches, the FDASH algorithm switches the video rate only if the predicted buffer level for the next 60 s increases above or decreases below a predefined threshold. If a video client has a small buffer, this approach minimizes video rate switches; however, it leads to more playback interruptions, which degrades QoE. Table 4 and Figure 7 indicate that the FDASH experienced the lowest number of video rate switches, but also experienced the greatest number of playback interruptions. The QLSA and DASH-Google algorithms achieved low video rates while experiencing more video rate switches, which degraded QoE.