## 1 Introduction  
With the development and innovation of 5G, Super-Fast broadband and WiFi 6 [1; 2], everyone anticipates that these developments will not only bring fast and reliable internet, but also significantly enhance user experience, particularly for real-time services. Increasing video streaming usage and the emergence of new forms of real-time applications, such as online game streaming and cloud gaming, are compelling network providers to increase spectrum capacity, decrease network latency, and improve other network parameters, in order to provide satisfactory service to users. Quality of Service (QoS) is a set of technologies and practices that are used to manage and prioritize network traffic in order to ensure that certain data flows, or groups of data flows, receive a certain level of performance. It involves optimising network parameters to fulfil the user's service level agreement (SLA). Metrics, such as throughput, delay, jitter, packet loss, etc., are used to measure QoS objectively. However, in the end, the demand for the relevant network providers is based on how the user feels about their experience. It has been proven difficult to evaluate the user's experience accurately and objectively. Therefore, until the recent past, network engineers constructed networks without actual user feedback. To understand the user's perspective, the quality of experience (QoE) metric was developed [3]. There are many different definitions of QoE. One of the main reason for this is that the experience of users is a really broad phenomenon, that exists in multiple disciplines. As a result, it becomes difficult to have a coherent and consistent definition of what QoE is. In this study, we stick with the popular understanding of what QoE is for multimedia applications under telecommunication systems. In the 2019 amended version of the ITU-T Vocabulary for Performance, Quality of Service, and Quality of Experience [4], QoE is defined as \"the degree of delight or annoyance of the user of an application or service\". However, they also mentioned that this definition of QoE is a work in progress and subject to change as research in this area is ongoing. Recognising the importance of the user's perceived experience, service providers and researchers became eager to identify QoS and QoE correlations. Several studies focused on finding how various QoS parameters affect the user QoE in telecommunication systems [5; 6]. By using QoS metrics in this manner, there was a way to oversee the end user's experience. Instead of using simulation, network emulators have been extensively used to test the performance of such models and validate theory. Therefore, it is vital to have emulators that can accurately replicate real networks. Due to their accessibility and peer assistance, open-source emulators are popular among network researchers. One such packet network emulator, which is popular in both academia and in the communication industry, is NetEm. However, authors in [7; 8] noted that NetEm has a number of significant shortcomings that hinder its capacity to replicate realistic packet traffic and packet network systems. One of the limitations is its lack of ability to correlate between various types of network QoS metrics; this is significant, as the correlation between QoS metrics is common in real-world network systems [9]. For example, in a simple queuing system, as the delay of the packets increases, the probability of packet loss also increases. This indicates that delay and loss QoS metrics are cross-correlated in some form. The increase in delay also increases the likelihood that the next packet will also be delayed. This can be a significant disadvantage, since the emulation lacks the realistic nature of internet buffering. In addition, the emulator cannot accurately simulate the long-tail packet waiting times observed in actual Internet traffic [10].