###### Abstract  
DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user's Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user's QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client's bitrate selection in dynamic adaptive streaming media services. mobile video; quality of experience; metric; deep spatial-temporal representation; DASH 1 2020 10 1793 10.3390/app10051793 10.3390/app10051793 10.3390/app10051793 10.390/app10051793 10.3390/app10051793 10. QoE refers to a user's experience on the quality and the performance of the device, network, system, application and services [3], which reflects the satisfaction or the comfort of the users while using the service. In [4], QoE has been defined as \"The degree of joy or annoyance experienced by a user while using an application or service.\" Compared to Quality of Service (QoS), QoE takes the \"human\" factor into account for focusing on the user's subjective perception of service quality. There are various influencing factors that may affect QoE from all aspects of the communication system. Many researchers have proposed different QoE assessment models to study the factors influencing QoE for mobile video services for obtaining remarkable results. For exploring mobile video QoE assessment, it is essential to find a relationship between the influencing factors and the Mean Opinion Score (MOS) value. The modeling process is mathematically expressed as:  
\[Y=f(X) \tag{1}\]  
where \(Y\) refers to the user's QoE that is usually measured using the MOS value and \(X\) refers to the subjective and objective factors that influence the QoE. Hence, video quality assessment can be described as a multi-index assessment problem and the key to modeling is to find an optimal mapping relationship, i.e.,\(f\) (\(\cdot\)). In order modelling the QoE, we firstly discuss the assessment of QoE for mobile video services, which includes video quality and the user's experience. * Video quality  
Video quality can be assessed in a subjective or objective manner. Subjective quality assessment methods allow the observers to make direct judgements on the quality of the video. The subjective assessment used in this study is the MOS. It involves classification of subjective feelings experienced by a person on watching a video into five different levels, in which each level represents a certain range of acceptance. The subjective assessment results are highly accurate and reflect a user's intuitive experience; however, they are difficult to implement on a large scale in practical applications. The objective video quality assessment is evaluated by reconstructing the distortion degree of the video in comparison to the original reference video.