## 7 Conclusions and Future Works  
This research provides a novel technique to deal with challenges posed by limitation of network emulators. We modified a popular network emulator to replicate real network \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Stutter-Type** & **PLR(\%)** & **FIFA** & \multicolumn{3}{c}{**CSGO**} & \multicolumn{3}{c}{**GTA**} \\ \hline  & & **Non-Corr** & **Corr** & **Non-Corr** & **Corr** & **Non-Corr** & **Corr** \\ \hline  & 2 & 0 & 2 & 0 & 2 & 0 & 0 \\ Short & 5 & 1 & 7 & 2 & 4 & 0 & 2 \\  & 10 & 8 & 16 & 5 & 8 & 4 & 4 \\ \hline  & 2 & 0 & 0 & 0 & 2 & 0 & 0 \\ Long & 5 & 2 & 2 & 2 & 5 & 0 & 0 \\  & 10 & 3 & 6 & 3 & 5 & 3 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Avg. Number of Short/Long Stutter Events for Correlated vs Non-Correlated Scenarios. operations more precisely and analysed how it affected end-user QoEs, both subjectively and objectively, for interactive cloud-gaming applications. We utilised real-life internet data to build empirical distributions and to analyse cross-correlations across network metrics to solve NetEm's lack of accurate waiting-time distributions and correlated QoS parameters. Next, subjective QoE evaluation for upcoming cloud gaming apps was used to compare correlated 'Delay-Loss' with the default non-correlated NetEm setup. Human test subjects showed that correlated, versus non-correlated, QoS indicators affected cloud gaming QoEs differently. Video-QoE and game-QoE did not differ considerably in a non-correlated scenario. In correlated-QoS, human users interpreted video and game QoEs differently and we observed game-QoE as being more influenced by network degradation than video-QoE. To confirm our subjective QoE analysis findings, we analysed the experiment's video objectively. We assessed how well Full-Reference (FR) VQA measures represented subjective QoE. Non-correlated QoS demonstrated a stronger correlation between FR QoE metrics and subjective MOS than correlated QoS scenarios. We also saw how taking packet duplication into consideration could be used to model a NR metric that could predict the user QoE with as much accuracy as FR VMAF metric. Finally, we found that correlated scenarios had more stuttering than non-correlated scenarios, which made the quality of the game experience (QoE) worse. It is evident from both the subjective and objective QoE analysis of cloud-gaming applications that it is important to take correlated QoS network condition into account when modelling for QoS to QoE metrics. Subjective analysis clearly showed how video and game QoE vastly differ in correlated versus non-correlated scenarios. The existing objective metrics are geared towards analysing the video QoE generally, and would be unable to correctly identify situations where a user experienced a bad game-QoE, even though the video-QoE was satisfactory. This could lead service providers to incorrect conclusions regarding the level of end-user satisfaction based on QoS information. In the future, we intend to explore significant quality indicators that could be utilised to more accurately estimate the game-QoE in a correlated QoS situation. Additionally, we need to collect more user-rating data, as the data used in this study was still limited in quantity. This should enable us to extend our investigation in order to derive mathematical correlations between various QoS parameters and game-specific KQIs in order to model the QoS-QoE metric for cloud-gaming applications. In addition, we also plan to investigate whether the effect of correlated Delay and PLR on other interactive applications, such as online interactive classrooms, is comparable to what we observed in this study. This experiment was completed in the QMUL networks laboratory under the supervision of [PERSON]. The project conceptualization was done by [PERSON] and [PERSON]. This paper's emulator was modified by [PERSON] and experiments were conducted together with [PERSON].