## 5 Subjective QoE Evaluation of Cloud Gaming
### Subjective QoE Results
Finally, we could also see the difference between correlated and non-correlated scenarios for both video and game QoE becoming apparent once the PLR > 2% This was understandable, given that the Parsec streaming architecture was highly effective at handling low-level PLR, thereby minimising the potential impact of PLR events and resulting in similar user ratings. However, when the PLR was large, the streaming application's packet loss recovery had a hard time keeping up, and the difference in the way PLR implemented in correlated and non-correlated scenarios was reflected in the user ratings. This concept is examined in further depth in the objective analysis section. To understand the magnitude of difference between the video-QoE and the game-QoE, we decided to plot the percentage differences between the video-QoQ and game-QoE under different PLR scenarios, as shown in Figure 7. It was clear from the figure that video-QoE and game-QoE behaved differently under correlated and non-correlated QoS scenarios. In non-correlated \"Delay-Loss\" scenario, video-QoE and game-QoE showed very small differences. Only at the 10% PLR scenario, did GTA show much higher game-QoE, and FIFA showed much higher video-QoE. Other than that, both video and game QoEs were quite comparable for all three games. However, in the scenario, all of the games showed better video-QoE than game-QoE. The difference was small when PLR was less than 1%. However, as PLR increased to more than 1%, the difference between video and game QoE kept increasing until almost 60%. This was quite a surprising finding, since there were some studies which mentioned how gamers give importance to game-playability more than video. These studies never showed such big differences between them. This result showed how important it is to consider cross-correlated QoS for interactive applications like cloud gaming. To understand the extent of the difference between video-QoE and game-QoE, we decided to plot the percentage differences between the video-QoE and the game-QoE under different PLR scenarios, as shown in Figure 7. The graph shows that video-QoE and game-QoE reacted differently under correlated and non-correlated QoS scenarios. In non-correlated \"Delay-Loss\" scenarios, the differences between video-QoE and game-QoE were minimal. Only in the 10% PLR scenario did GTA exhibit a significantly higher game-QoE  
Figure 7: Percentage difference in Video-QoE and Game QoE with and without correlation. and FIFA a significantly higher video-QoE. Aside from this, the video and game quality of experience for all three games were comparable. In correlated scenarios, however, the video-QoE was always higher than the game-QoE for every game. When PLR was less than 1%, the difference was minimal. However, as PLR increased by more than 1%, the disparity between video and gaming QoE continued to grow until it reached about 60%. This was a somewhat an unexpected finding. Even though there are studies [25] that have indicated that gamers place a greater emphasis on game-playability than on video, they never demonstrated such a large gap between the two QoEs. This research illustrates the significance of cross-correlation in interactive applications such as cloud gaming.