###### Abstract
\(QoE\) is a multidisciplinary concept represented in various fields, such as psychology, ergonomics, marketing, information technology, artificial intelligence, social sciences, etc. This wide representation is one of the main reasons for the lack of a single definition of this term, as well as a list of factors that influence its level [5]. However, in the next section, several definitions that are most often used will be presented. According to [6], \(QoE\) can be defined as \"A measure of user performance based on both objective and subjective psychological measures of using an ICT service or product\". The Standardization Sector of the International Telecommunication Union (ITU-T) defines \(QoE\) as \"The overall acceptability of an application or service, as perceived subjectively by the end-user\" [7; 8; 9]. In the technical specification of the European Telecommunications Standards Institute (ETSI) [5] and ITU-T recommendation [10], \(QoE\) is defined as \"the degree of delight or annoyance of the user of an application or service\", which is also one of the current definitions of this term. In addition to regulatory bodies, numerous researchers formulated their definitions and on the basis of them created certain models for assessing the quality of experience. According to [11], \(QoE\) represents a subjective user assessment of service quality, and the most frequently used qualitative ratings are: \"Good\", \"Poor\", \"Fair\", \"Bad\". It is the state of the field, with a multitude of definitions and ways to measure \(QoE\), that opened the door for future research into this concept. Most published models present \(QoE\) via mean opinion score (MOS) for a group of users. In this paper, the quality of user experience at the level of an individual user \(QoE_{\rm i}\) is observed as a dependent/output variable, where \(i\) ranges within 1...n users. This means that the arithmetic mean of \(QoE_{\rm i}\) values corresponds to the total \(QoE\) for a group of users expressed through MOS. Today, special attention is paid to modeling \(QoE\) in user interactions with video streaming services, which represent the most significant part of total mobile internet traffic [12]. This progress in the field of video streaming resulted in the rise of both video-on-demand services (YouTube, Netflix, Amazon Video, Hulu, etc.) and live (Twitch.tv, YouTube Gaming) streaming [13]. In order for providers to perform constant monitoring and ensure a satisfactory level of \(QoE\) for the mentioned services in the future, special emphasis is now placed on models based on machine learning techniques [14]. The main goal of this research is to model the overall quality of user experience in interactions with telecommunication services that belong to the conversational, streaming, interactive and background classes of network traffic. For the case study, the three largest mobile operators, as providers of the aforementioned services, operating on the territory of the Republic of Srpska and Bosnia and Herzegovina were chosen: Mtel, BH Telekom and HT Eronet [15]. The hypothetical setting is that the individual user's curiosity, creativity, communication, personality, courage, confidence, charisma, competence, common sense and memory are adequate transition variables in a sustainable \(QoE\) model. The second assumption is that the use of transitional variables of the sustainable quality of individual user \(QoE_{i}\) can be a very important parameter for planning and optimizing network resources and realizing the competitive advantage of individual service providers in the future. The following contributions of this paper can be clearly identified:  
* This paper uses a subjective approach to assessing the level of user experience, which is based on a unique questionnaire with 11 questions. * Survey questions were formulated based on the selected original set of five factors affecting \(QoE\): (1) legal-regulatory; (2) technological-process; (3) content-formatted and performative; (4) contextual-relational; (5) subjective-user.