# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 1 Introduction  
Over the last few years, the number of subscribers as well as the traffic demand on mobile communications networks have been dramatically increasing. With the arrival of the fifth generation (5G) of mobile communications networks, an increment in the number of services, functionalities, and users is expected [1]. The emergence of new 5G services that require many radio resources and the growth of users' expectations have motivated the search of new techniques to optimize mobile networks. Traditionally, mobile network optimization tasks have been focused on the analysis and improvement of certain key performance indicators (KPIs) of the radio access network (RAN), such as the call block rate or the call drop rate [2]. With the constant evolution of the technologies used in mobile networks, optimization techniques have been changing over time, focusing increasingly on the quality perceived by final users from a subjective perspective. To this end, optimization based on certain key quality indicators (KQIs) has been recently proposed [3]. KQIs are metrics defined from the computation of different indicators, such as KPIs [4]. KQIs provide information at the application level about the quality of the services offered to the users following an end-to-end vision. Following this line of thought, in [5] a study of possible improvements in mobility in mobile communications networks is presented with the aim of reducing the interruption time of services. The current trend is focused on the optimization of quality of experience (QoE) metrics. QoE metrics can be directly obtained by conducting tests to users or by using mathematicalmodels that are based on KQIs to approximate the opinion of real users. These techniques reflect the final users' satisfaction. The authors in [6; 7; 8; 9; 10] address the optimization of the QoE perceived by the users of a video streaming service from an application layer perspective. In this way, they use such metrics as the video initial time, the receptor buffer state, or the application level transmission rate to evaluate the QoE. In studies such as [11; 12; 13], a radio resource scheduler based on the QoE perceived by users is proposed. In [14], the adjustment of physical layer parameters is carried out to optimize the QoE. Due to the typical interference, noise, and fading of wireless communications, the signal sent by a transmitter will become degraded. A radio link control (RLC) layer provides a reliable link between a transmitter and receiver. This layer performs error detection and correction tasks in order to reduce the impact of the signal degradation. In this way, the RLC layer is responsible for the delay-reliability tradeoff and has a great impact on the QoE. In the literature, some studies have addressed optimization based on the adjustment of RLC configuration parameters. For example, the authors in [15] presented universal mobile telecommunications system (UMTS) technology. A performance evaluation of the user datagram protocol (UDP) and the transmission control protocol (TCP) in two of the RLC operation modes--AM and unacknowledged mode (UM)--is presented in [16]. Nevertheless, in this work, the authors focus on establishing the advantages of using one mode over another, without going into more detail regarding how to optimize the functioning of each of these RLC operation modes. Furthermore, none of these studies discusses the effect that a misconfiguration of the RLC layer may cause on the QoE. In [17], a preliminary analysis of how the RLC configuration parameters affect the QoE perceived by the users is presented, but this work does not propose a method to optimize it. In this paper, we propose the optimization of the QoE perceived by the users based on the adjustment of the RLC layer's configuration parameters. To this end, two types of services that present different characteristics have been selected--real-time video streaming and file transfer--allowing us to more deeply study the different operation modes of the RLC layer.