###### Abstract
The study points out that many short stalls can be more annoying than a single long stall. Additionally, 70.83% of test subjects answered that stalls are the most relevant metric when evaluating the quality of the video. 16.67% of users considered quality the most relevant aspect [9]. The Hammerstein-Wiener predictor has been used to create a QoE evaluator called time-varying QoE Indexer, which accounts for interactions between stalls, analyzes video content and perceptual video quality, and predicts continuous-time QoE. The proposed predictor considers the number of stalls and their length, time since the last stall, frequency of stalls, rebuffering rate, buffer model, scene criticality, and perceptual quality [10]. An unsupervised learning approach has been studied to assess the QoE in IPTV services along with self-organizing maps. That approach included a full-reference model, visual distortion, and packet loss considerations and was suitable for assessing the video quality in broadcasting TV systems [11]. Another study that investigated unsupervised learning for QoE measurement presented the real-time algorithm deployed on the server side and the offline no-reference assessment on the customer side. A restricted Boltzmann machine (RBM) was trained based on, inter alia, bitrate, video motion, or blur mean metrics and sent to the customer side to execute the detailed measurement in real-time [12]. The event-based perceptual quality (EPQ) framework has been introduced to estimate the QoE concerning rebufferings and memory mechanisms. The measurement is performed in a real-time scenario, and the perceptual quality can be returned at any time. The EPQ output is consistent with the logarithmic nature of the human perception system regarding video impairments insight [13]. Back-propagation neural networks and random forests were applied to assess stallings and initial buffering delays in the QoE estimation. Metrics evaluated based on encrypted HTTP traffic are common in video transmission, specifically from the perspective of network operators [14]. Back-propagation neural networks have likewise been utilized to assess video quality based on quantum place, bitrate, and motion vectors. Research has mainly analyzed the compression and network transmission damage and mapped it to the mean opinion score (MOS) grade [15]. Ridge regression, a three-dimensional convolutional neural network, and LSTM were used to build a QoE assessment model that analyzed video quality, fluency, and volatility. During the publication, the proposed model can outperform state-of-the-art models. However, the model complexity may be a drawback for real-time analysis by network operators [16]. Random forest, neural network, and LSTM algorithms were used to create a QoE assessment model to analyze YouTube videos. Those models applied the initial playback delay, video streaming changes, quality, and buffering to return the quality prediction [17]. The SSIM metric and neural network have been implemented to predict the quality of videos encoded with H.264 and H.265 codec by one mapping function. The model is dedicated to IPTV systems and returns the ACR grade [18]. Motion vectors, spatial image features, and transmission impairments were utilized to develop the assessment model that includes the SVR regression to return the mean opinion score value [19]. LSTM network was also utilized to assess the quality of HTTP streaming sessions based on bitstream-level parameters, stalling effects, and padding of adaptive streaming segments. The segment MOS (S-MOS) has been found as the best metric to return the segment quality grade. The proposed model has an RMSE of less than 0.479, depending on the analyzed dataset [20]. Another neural network approach forecasts the streaming video quality and degradation before the user notices it. The paper proposed a time series solution based on BiLSTM-CNN and compared it to solutions based on SVR, MLP LSTM, and BiLSTM methods.