# Multi-Layer QoE Learning System Implemented by Fiberhost
## 1 Introduction
### Related Works  
A number of studies introduce various QoE models that can be utilized by network operators in practice. Predictive models based on automatic modelling, decision trees and linear regression have been used to estimate the sustainable QoE level based on the survey referenced to contextual-relational, legal-regulatory, subjective-user, technological-process, content-formatted and performative factors [8]. The Q-meter system has the advantage of detecting customers' complaints using deep learning-based models. The sentiment analysis can be applied to determine the cause of decreased quality level and detect service failures within cellular networks. This real-time system uses complaints posted on online social networks for degradation detection [9]. The full-reference method, alongside supervised and unsupervised algorithms, has been used to estimate the QoE level for multicast-based video environments. The QoS parameters include delay, jitter, and packet loss, as well as APSNR, SSIM, and VQM video parameters and have been used to achieve the required QoE level and adapt the video signal to increase the perceived quality. From the network operator's perspective, estimations can be performed for a single or group of viewers due to tv channel clustering [10]. WebQoE based on decision trees, kNN, and SVM have been introduced to evaluate the QoE level based not only on video-related parameters i.e., initial delay, stalls, or quality switches, but also on user behavior and social context. Moreover, the authors introduced the QoE influence factors (IFs) classification to system, human, context and service metrics [11]. The data-driven approach for video quality estimation has been proposed to assess QoE in a mobile environment. 89 network features related to a mobile network, video characteristics and playback were gathered by mobile applications. In addition, the MOS distortion grades on a 1-5 scale were obtained. Over 80,000 samples were used to train and evaluate the deep neural network in a batch manner [12]. D-DASH framework that combines deep learning and reinforcement has been proposed to assess the QoE level for video streams based on the HTTP DASH (Dynamic Adaptive Streaming over HTTP) standard. Authors employed feed-forward and recurrent deep neural networks to utilize features related to the video quality of DASH segments and playback stalls [13]. DeepQoE is an end-to-end framework that combines word embedding and a 3D convolutional neural network to extract generalized features and fed neural networks for representation learning. Regression and classification are used to retrieve the final assessment. This novel approach has the advantage of calculating a QoE score in different environments [14]. The idea of unsupervised deep learning techniques makes it possible to perform quality assessment in an online mode. The no-reference features related to the pixel and bitstream layer were employed to train the restricted Boltzmann machine model in an offline mode, which then can be sent to the client side to assess the continuous approach. The model was evaluated with the LIMP Video Quality Database [15]. The content-aware and distortion features were used to feed the GRU neural network to assess the video sequence quality. Distortions that occurred during video acquisition and those related to compression or packet transmission were detected, and as a result the video quality was assessed by a single neural network. Additionally, the authors proposed a pooling mechanism to detect distorted frames that significantly influenced the perceived video quality [16]. The HA-SVQA algorithm has been proposed to speed up the process of subjective video quality assessment. The solution based on active learning eliminates less valuable video sequences, considering the image quality features, encoding, and statistical parameters.