## 2 Related Work  
Cloud gaming has emerged as an interactive application, where users can play resource-intensive games on thin clients. The idea is the application of utilising powerful computer devices to run the game in a remote data centre, streaming the game play to the user over the internet, capturing their input, and returning it to the host computers in the data centre. This is the reason cloud gaming is also sometimes referred to as Gaming on Demand, as the user can play it conveniently wherever they desire without having high-spec hardware to run the game [11]. Cloud gaming requires a lot of data to be transferred from the host (cloud) to the user (client) over the internet. This transfer of data packets through the network makes network quality a significant factor of the end-user experience of cloud gaming applications. A higher delay in the network can result in delayed user responses that can lead to poor game control, and vice versa [12]. In the same way, a higher packet loss in the network means that key game information is lost and video quality goes down, which makes for a bad user experience. There are many studies that look at the effect of these network parameters on the user experience of cloud gaming, but they miss how these network parameters also have correlations between them, which can have a significant impact on the quality assessment of cloud gaming. This study aimed to fill that gap in the literature by demonstrating how PLR and correlated delay affect the end-user gaming experiences. The majority of studies that examine the effect of network traffic on the quality of the cloud gaming experience use a network emulator to recreate the cloud gaming experience in the laboratory [13; 14; 15]. NetEm is one of the most popular network emulators that are used in both academia and industry. NetEm provides multiple benefits, such as the use of custom delay distribution, support for heavy tail distributions, and auto-correlation between different network parameters, but it still lacks some key network principles that need to be evaluated for accurate mimicry of the network traffic. The most commonly used uniform or Pareto distribution of the Netem does not represent realistic network delay distribution. Refs. [16; 17] argue that network delay distribution follows a log-normal distribution, which is not available in Netem by default. In addition to the delay distributions, the packet loss implementation in NetEm is not realistic. Ref. [18] NetEm drops packets randomly to achieve the desired packet loss levels. However, in many network configurations, packet loss is caused by full packet buffers, resulting in a correlation between high packet delay and discarded packets, which is what we mean by QoS parameter correlation. NetEm provides a Gilbert-Elliot two-state model where subsequent packet loss can increase if some packets are lost in the current time period. However, this loss is still not correlated to the accumulation of delay. Furthermore, the authors in [7] reported that network emulators do not consider the correlation between different QoS parameters. Most emulators assume that packet loss, delay, and jitter are independent of each other. Some studies, such as [16; 19; 20] showed that different QoS parameters could be correlated. The authors reported a mathematical relationship between loss, delay and jitter and established that QoS parameters were cross-correlated. Other studies that use NetEm to study the effect of packet loss, delay and jitter on the QoE of video applications include [21; 22]. Most of these studies use the default configurations of NetEm and, hence, do not take into account cross-correlation between QoS metrics; thus, limiting their findings. There are multiple accounts where the quality assessment of cloud gaming under various network conditions was tested. Ref. [23] is one such example. The study presented network performance analysis for various games hosted on the onLive gaming platform.