## 3 Methodology
### QoE Models for Video on Demand Applications  
VoD applications like video streaming employing UDP were used to map the effect of changing QoS parameters on QoE. We used the Linux-based network emulator NetEm to change the magnitudes of PLR and jitter. Our testbed includes a host Dell computer streaming the video to the subject Dell computer via NetEm via Ethernet cables. VLC was used as a media player to stream the video between the host and subject machines. We decided not to make use of default NetEm delay distributions due to their limitations as mentioned in [31]. Rather we used measurement data from Teragence to determine the delay distribution. It was found that the empirical delay distribution follows a lognormal distribution. Different delay distributions were obtained from the empirical data, and these provided a platform to generate delay distributions whose inherent variability provided different realistic \"jitter patterns\" for the emulations. At the initial testing of the NetEm testbed, it was observed that larger jitter values (larger than half of the mean delay) introduce packet reordering in the emulated packets. Since we only wanted to see the impact of jitter without packet reordering, we increased the rate function in NetEm. We also added a constant delay to the distribution so that, jitter values never get larger than half of mean delay. This ensures that the jitter patterns remained the same without packet reordering. However, it does increase the mean delay of the delay distributions. This does not affect the outcome of the experiments since a constant delay is not an influencing factor for QoE of video streaming as reported in [29]. We tested this by streaming the test video using constant delay and no effect on quality was recorded apart from longer start time. A 2 min long video of a car advertisement was used in this study fulfilling the spatial and temporal information requirements mentioned in ITU-T recommendation P.910 [32]. The video was encoded using lossless H.264 and a frame rate of 30 fps was configured. The resolution was set to 720p and fixed bitrate of 1200 kbps was used in all experiments. Moreover, the lighting of the room was set according to ITU-T recommendation BT.500 [33] and the subjective testing was carried out using ITU recommendation P.910 for subjective multimedia testing. We used subjective testing to evaluate the QoE of video streaming at different PLR and jitter magnitudes. A minimum of 4 subjects are recommended by ITU-T for subjective testing of videos in P.910. We use 26 subjects in this investigation with ages between 18 and 45 and with no visual \begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Providers** & **PCC between jitter and PLR** & **SCC between jitter and PLR** \\ \hline
**A** & 0.22 & 0.18 \\
**B** & 0.19 & 0.16 \\
**C** & 0.28 & 0.19 \\
**D** & 0.35 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Linear and non-linear correlation coefficients between packet loss ratio (PLR) and jitter. disabilities. Each subject was shown the video at five different operating points of PLR and jitter without telling them the network configuration to avoid bias. At the end of the video, the subjects were asked to rate the video according to Absolute Category Rating scale and the rating were used to calculate the MOS at that network configuration. Only one network parameter was changed at one time to see the effect of each parameter on QoE individually. No prior training was required and the total time taken by 1 subject was almost 25 min. The collected data of MOS and regression analysis was used to derive the QoE expressions in terms of PLR and jitter. Since this is not the main focus of this research and we will not go into details of the performance of these models and see [11] for details. From these experiments, Equations (3) and (4) present QoE in terms of PLR and jitter, respectively. \[QoE=3.5*exp^{-120*PLR}+0.5*exp^{-400*PLR}+1 \tag{3}\]  
In Equation (3) PLR is measured as a ratio of lost packets to total sent packets.