## 1 Introduction
Despite many studies exploring the relationship between Quality of Service (QoS) and Quality of Experience (QoE), very few have considered the effect of correlated QoS factors on QoE for end users. This may be because of limitations in network emulation software or lack of access to real-world traffic statistics. As a result, the impact of cross-correlated QoS metrics on QoE for applications has not been explored by researchers, limiting the usefulness of previously published QoS-to-QoE mapping models. Additionally, the mapping between QoS and QoE is application-dependent. So, the way video streaming and VoIP applications are currently mapped is not suitable for new real-time applications, like passive game streaming, interactive game streaming, and cloud gaming. To provide the end user with a good experience, the mapping of these services needs to be done as quickly as possible. In order to accomplish this, it is essential that the emulator used produces cross-correlated QoS measures that are realistic. This research suggests a new way to work around these problems with network emulation and aimed to bring more attention to these problems. The primary contribution of this study was to modify NetEm so that it more accurately replicates real network processes through correlation between loss and delay and to analyse how this affects the end-user QoE, both subjectively and objectively, for cloud-gaming applications. We show how important cross-correlation analysis of QoS metrics is by partnering up with a network analytics company to utilise their extensive database of real-time QoS information for the top four UK internet providers. We employed these data to establish empirical distributions and perform cross-correlation analysis between various network parameters to address both the lack of accurate and precise waiting-time distributions and correlated parameters in NetEm. Next, the impact of correlated 'Delay-Loss' was compared with the default non-correlated NetEm configuration using subjective QoE evaluation for emerging cloud gaming applications. The results from the human subjects of the experiments demonstrated that the QoE of cloud gaming applications affected the subjects differently when they were subjected to correlated versus non-correlated QoS metrics. In a non-correlated scenario, the two different aspects of QoE, video-QoE and game-QoE, did not differ significantly. However, in correlated-QoS scenarios, there was a significant difference in in how the video and the game QoE were perceived by the human users. Interestingly, game-QoE was more affected by degrading network conditions than video-QoE in the correlated scenario. This could have a significant impact on the success of the cloud gaming industry, as the majority of existing QoS-QoE models were developed without QoS-correlation in mind. This would lead to an erroneous conclusion regarding the Game-QoE, which is the most important QoE factor in cloud gaming applications. To confirm our subjective QoE analysis findings, we performed objective QoE analysis on the subjective experiment's video. We evaluated the accuracy with which popular existing Full-Reference (FR) video quality assessment (VQA) metrics represented subjective QoE. Existing FR metrics showed a higher correlation to subjective MOS in non-correlated QoS than in correlated QoS scenarios. We also proposed a new No-Reference (NR) QoE metric that outperformed the existing FR metrics. Finally, we compared the average number of stuttering events in correlated and non-correlated network conditions. We believe this is the first study of its kind to examine how the QoE of cloud-gaming applications are affected by the correlated nature of actual internet traffic and to present correlated emulation for the first time as a wholly new paradigm for network performance evaluation.