## 2 Review of Relevant Published Research  
It is generally known that a real-time service that is very important for an enormous number of users, voice transmission over Internet protocol (VoIP), has been extensively researched with different goals. In [16], \(QoE\) modeling for VoIP service is presented by improving the simplified E-model with a subjective MOS model. The advantages of the final model in terms of accuracy and reliability are explained. On the basis of developing wireless network technologies, high-speed data transmission is now possible. Therefore, in the paper [17], the authors present a holistic modeling of \(QoE\) estimation when using live video streaming services in 4G networks. The main goal of the aforementioned research is the assessment and prediction of subjective \(QoE\) metrics, taking into account various variables related to QoS, bit stream and basic video quality metrics. The paper [18] presents a \(QoE\) model based on machine learning principles aimed at protecting the privacy data of users who participated in \"sensitive\" case studies. The basic idea in this orientation is that there is no need to share user datasets amongst researchers. Instead, each researcher collaboratively participates in partial model training. The paper [19] presents an approach that solves this problem using intelligent sampling and the results of experiments with similar outcomes. A generic \(QoE\) model for evaluating the quality of video calls within a Wi-Fi network is presented in the paper [20]. The authors proposed metrics that are independent of content, application and user equipment or device. Specifically, they selected the following \(QoE\) parameters: perceptual bitrate--PBR, freeze ratio, length and number of video freezes in real time. When it comes to the World Opera application for real-time video content transfer, the \(QoE\) quality assessment is modeled in the paper [21]. In a very complex solution of the model, the video streaming of several musical artists, spatially dislocated, are combined into one video. The assessment of the level of \(QoE\) in this case is determined on the basis of an indicator called _perceived reliability_, because the quality directly depends on possible failures of some of the end-to-end software, hardware or network components, which are included in the implementation of this telecommunication service. In addition to \(QoE\) models, which are mainly related to streaming traffic today, other services should also be highlighted. Thus, in the paper [22], the authors propose a Web \(QoE\) model for evaluating the level of user experience when using interactive services using a Web browser. This model is based on psychological characteristics of the user, which are connected with their memory, that is, the memory of the experience gained in the previous period. Similar research related to the modeling of the quality of experience for Web applications (QoEWA) is presented in the paper [23]. This approach integrates key performance indicators (KPI) and key quality indicators (KQI) [24]. The four categories of factors affecting \(QoE\) in the paper [25] are: human-related influencing factors; system-related influencing factors; context-related influencing factors; service-/application-content-related influencing factors. The focus of the observation is audio and video streaming services available through wireless and mobile technologies (4G, 5G). Regression algorithms of machine learning (artificial neural networks) were applied in research [26] to map QoS parameters onto \(QoE\) level for VoIP services. In the paper [27], the authors model the dependencies of the subjectively assessed level of \(QoE\) and technical network parameters obtained via measurements when using web browsing services. The recognizable aim of the paper is to connect subjective \(QoE\) with measurable parameters of service quality, which can be monitored in an operational environment, enabling an objective and real assessment of \(QoE\).