### QoE Assessment Model Based on Machine Learning  
The machine learning-based method uses many training data samples to establish a mapping relationship between the various influencing factors and the subjective assessment results. In comparison to the optimal mathematical modeling method, this method can account for various influencing factors that are complex in nature and affect the QoE. This model is found to be highly accurate. In recent years, many works [12; 13] have used the QoE prediction model established by machine learning to guide the client's bitrate selection in dynamic adaptive streaming services. Reference [14] summarizes the methods used for building the QoE assessment models based on machine learning. Reference [15] uses the Hammerstein-Wiener method to develop the model for analyzing a user's QoE at the time of re-buffering. Reference [16] accounts for three factors, namely, the video quality, re-buffering time and memory to devise the QoE prediction model using Support Vector Regression (SVR). The P.NATS [17] uses the random forest approach to set up a model that would enable us to analyze the effects of re-buffering location, re-buffering time, frame rate and video quality on a user's QoE. Reference [18] proposes a multi-layer neural network based on the SGD-BP algorithm to set up the QoE model. Reference [12] uses the deep reinforcement learning method to perform the client's bitrates selection. First, a chunk-wise QoE model is established using three influencing factors such as video bitrate, quality switching and initial re-buffering time. The QoE model is used as a reward function to affect the client's bit rate selection. Then, a linear function is used to reflect the relationship between the influencing factors and the QoE. Finally, SVR is used to determine the coefficients of each of the variables. The experimental results demonstrate that this QoE model is better than the model thzt has been used in the Pensieve method [19]. In References [20; 21], the effects of spatial and temporal characteristics of a video on a user's QoE have been considered. Spatial Information (SI) and Temporal Information (TI) are considered to be major influencing factors while developing the QoE model. The experimental results indicate that the temporal and spatial characteristics of a video help to improve the accuracy of the QoE model. In spite of successful results, there is scope for improvement in certain areas such as:  
1. For the existing methods, SI and TI are usually used to explain the characteristics of the content of a video. However, owing to its complexity, SI and TI are unable to explain the characteristics. 2. The volatility of video quality caused due to bitrate switching has not been taken into account sufficiently. This paper proposes to develop a QoE assessment model based on the Dynamic Adaptive Streaming over HTTP (DASH) standard which shall consider various factors such as characteristics of the video content, video quality, playback fluency and quality volatility, average bitrate, re-buffering time and count, initial buffering time and quality switching amount. The parameters that define these influencing factors are combined together to form a feature parameter vector, and the ridge regression method is adopted to map the relationship between the feature parameter vector and the MOS value. The experimental results on the two public datasets depicts that deep spatial-temporal features can effectively improve the accuracy of the QoE model. Moreover, the proposed QoE assessment model exhibits higher accuracy in comparison to the state-of-the-art QoE models. The main contributions of this paper are as follows:  
1. The spatial and the short-term temporal features of a video are extracted using Three-Dimensional Convolutional Neural Networks (3D CNN). The Long Short-Term Memory (LSTM) is then used to extract the long-term temporal features of the video. 3D CNN and LSTM are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. 2.