## 5 Subjective QoE Evaluation of Cloud Gaming  
Equipped with the bespoke emulator, we decided to carry out subjective testing with human subjects playing three different games under different correlated and non-correlated \"Delay-Loss\" QoS scenarios. Since this was an interactive application, this research considered two QoE criteria. The first criterion was the quality of the game itself, in terms of how easy or difficult it was to play the game at a satisfactory level. The second criterion was the quality of the game's video. The video-QoE examined solely the image/frame quality of the video. Research has demonstrated that immersive systems, like VR, can affect the user's Quality of Experience (QoE) in gaming applications [33]. However, in this study, the focus was limited to the QoE of video and game aspects only, and immersiveness was not considered for practical reasons. In terms of various network conditions, we chose the peak-hour log-normal delay distribution with a mean delay of 50 ms to maintain consistency with our past findings. After reviewing similar experiments conducted by other researchers, seven different packet loss ratio (PLR) were chosen which were the following: 0.1%, 0.25%, 0.5%, 1%, 2%, 5% and 10%. So, for each game, there were 14 scenarios: even with correlated \"Delay-Loss\" and seven with non-correlated \"Delay-Loss\". To maintain consistency with the prior section's findings, the correlation value was selected to fall between 0.3 and 0.5.  
After careful consideration, it was decided to implement the correlated QoS metrics only on the incoming packets. This was done since the vast majority of the packets in the cloud gaming scenario are going from server to client. All the video frames and audio packets are coming towards the client, whereas only the key inputs from the client are being sent to the cloud server. Other studies [25] have shown how network degradation to packets going from client to server has minimal impact on QoE since very few packets are being sent to the server.  
The three game genres were chosen based on their popularity, genre, and content. The selected games were:  
* FIFA 2019 (Sports Genre)
* GTA V (Action/Adventure Genre)
* Counter Strike Global Offence- CSGO (First Person Shooter Genre)  
Using Spatial Information (SI) and Temporal Information, we subsequently analysed the content complexity (TI) of the three games. SI measured the amount of spatial information present in each frame of the game, whereas TI measured the amount of temporal changes that occurred between the frames. Therefore, a high SI implied that the game had a high level of image detail, while a high TI suggested that the game had rapid changes between each frames.  
In our prior work [15], we covered in greater depth the game selection procedure and the content complexity of each game. In general, they were added to cover a wide range of gaming applications based on the selection criteria in ITU-T G.1032 and G.1072 [34; 35]. Figure 5 depicts the results of the content complexity evaluation. The SI and TI findings showed that FIFA had the lowest SI and TI, indicating that it was a game with a slow tempo. GTA V had the highest SI and TI and was regarded as a high quality, quick-paced game. The content level of CSGO fell between that of FIFA and GTA, and it was classified as a medium-paced game.  
The objective of evaluating the SI and TI of each game was to determine whether varying QoS parameters had an impact on the QoE of the games, due to their complexity. This could potentially provide a future method for classifying QoS-QoE mapping, based on the genre or type of game.