## 1 Introduction  
According to Cisco estimates, in 2021, video traffic was subject to exponential growth rates corresponding to 82% of all consumer IP Internet traffic. This equals a million minutes of video traffic crossing global IP networks every second [1]. However, factors associated to network parameters, such as jitter, delay, packet loss [2], network capacity, unstable bandwidth, diverse terminals, user attributes and user interest in content can deteriorate the user experience. This user experience defines the end user's perception of a service and is known as Quality of Experience. The term QoE is described by ITU-T in Recommendation P.10/100 [3] as follows: \"a user's degree of delight or annoyance in using an application or service\". In [4], the authors proposed two methods--subjective and objective--to assess QoE. The authors in [5] proposed a classification scheme, where the objective methods are known as Instrumental Quality Models; these models are those that obtain information directly from the video package or stream [6]. Objective and subjective methods have been commonly used to assess QoE. Below, we provide an explanation of the main features of each method. Subjective methods employ user surveys, whereby individuals evaluate a service in a real environment, answering a questionnaire that reflects the characteristics of the service, providing the supplier with a quantitative indicator of QoE for the evaluated service. Users evaluate several aspects of the service using a discrete scale (5--Excellent, 4--Good, 3--Fair, 2--Poor and 1--Bad), and the mean opinion score (MOS) is used as the metric for each aspect [7]. ITU-R and ITU-T have several recommendations for performing subjective video assessments, including methodologies for subjective assessment tests, criteria for observer selection, assessment procedures and data analysis methods. Such recommendations are addressed in BT.500-13 [8], ITU-R BS.775-1, ITU-R BS.1286 [9] and ITU-T P.910 [10]. Objective methods are designed to overcome disadvantages inherent in the use of subjective methods; i.e., the cost and time involved in applying the surveys. The objective approach is based on mathematical (algorithms) and/or comparative techniques that generate a quantitative measure of video quality to analyze the presence of impairments, such as jerkiness, frame skips, freezes and tiling. Objective methods are classified into eight categories [6]:  
1. Reference-based classification method. This is based on the need to analyze the original signal to obtain the QoE measure for the video. It features three categories: 1. Full Reference (FR) Model: Models in this category measure the degradation or impairments in a video stream, comparing the video received to the original video [11]. 2. Reduced Reference (RR) Model: Models in this category analyze the video stream using only an explicit reference or certain key parameters of the original video to compare with the video received [12]. 3. No Reference (NR) Model: These models do not require a reference video stream and analyze only the received video stream, seeking indications that reflect impairments in transmission [13]. 2. Image-based classification method. Models in this category analyze the visual video information through two approaches: 1. The psychophysical approach, based on characterizing the mechanisms of the human visual system (HVS), such as contrast sensitivity, adaptation to color and illumination and masking effect [14]. 2. The engineering approach, based on analysis and extraction of distortion patterns and compression artefacts [15]. 3. Input-data-based classification method. This is based on the information obtained from level 3 and 4 headers and features five methods [16]. 4. Media-layer models, whose input is the media signal [17]. 5. Parametric packet-layer model, whose input is the packet-header information [18]. 6. Parametric planning model, whose input are the quality-design parameters [19]. 7. Bit stream layer model, whose input are the packet-header and pay- load information [20].