## 2 Overview of QoE Models  
In the context of video streaming, the concept of Quality of Experience (QoE) is referred to as _the extent users are annoyed or delighted with videos provided by applications or services_[3; 23; 24]. In the literature, many QoE models have been proposed that use different inputs and modeling approaches to estimate the impacts of quality variations and stalling events. In this section, we present a brief overview of typical QoE models. For detailed classifications and discussions of QoE models, the readers are referred to other survey studies [3; 25]. In general, a QoE model can be characterized by five aspects, namely (1) the key influence factors, (2) segment quality metrics, (3) model inputs, (4) modeling approaches, and (5) session durations. Table 1 presents a comparison of typical models according to these aspects. Here, the key influence factors are initial delay, quality variations, and stalling. In addition, the modeling approaches could be simple analytical functions or complex machine learning models like deep neural networks. From Table 1, we can see that, with respect to the included influence factors, all these models take into account the impact of quality variations. The influence of initial delay and stalling events is also considered in most of the models, except _[PERSON]'s_, _[PERSON]'s_, and _[PERSON]'_s. For memory-related effects, they are included in the seven models of _LSTM_, _ATLAS_, _P.1203_, _CQM_, _biQPS_, _SQI_, and _KSQI_. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **Modeling Approaches** & **Segment Quality** & **Session** & **Session** & **Inputs Used to Represent the Impacts of Factors** & & \\  & **Approaches** & **Parameters** & **Demands** & **Demands** & **Impact of Quality Variations** & **Impact of Sailing Events** & **Memory-Related Effects** \\ \hline \multirow{2}{*}{COMF [22]} & Analyfield & Subjective quality metrics (e.g., MCPS) & 60-500 & Location of & Histogram of segment quality values & & Last window quality value \\  & & & Infraction of & Histogram of segment quality values & histogram of multiple durations & & Maximum window quality \\  & & & duration & duration & Average window quality value & & \\ \hline \multirow{4}{*}{hQPPS [27]} & Long-Short & Subjective quality metrics (e.g., MCPS) & \multirow{4}{*}{60-500} & \multirow{4}{*}{height delay in} & \multirow{4}{*}{LSTM network with segment-bits} & \multirow{4}{*}{LSTM network with sampling durations} \\  & Terminology & & & & & & \\  &  
To represent segment quality, _[PERSON]'s_, _[PERSON]'s_, _[PERSON]'s_, _LSTM_, and _CQM_ models employ the subjective quality metric of MOS while _[PERSON]'s_, _ATLAS_, _SQL_, and _KSQI_ models use objective quality metrics such as VQM, STRRED, and VMAF. On the other hand, for _[PERSON]'s_, _[PERSON]'s_, and _biQPS_ models, each segment is attributed by one or several bitstream-level parameters such as quantization parameter (QP), resolution, and bitrate. It should be noted that the _P.1203_ model can be optionally fed by either MOS values or bitstream-level parameters. Regarding model inputs, the _LSTM_ and _biQPS_ models are inputted by segment-basis parameters while the others employ various statistics on a session basis, i.e., calculated over the whole session such as the median and the minimum segment quality values. With regard to modeling approaches, simple analytical functions (e.g., a weighted sum, exponential and natural logarithm functions) are applied in seven models, namely _[PERSON]'s_, _[PERSON]'s_, _[PERSON]'s_, _[PERSON]'s_, _[PERSON]'s_, _CQM_, and _SQI_. Meanwhile, _[PERSON]'s_, _LSTM_, _ATLAS_, and _KSQI_ models utilize advanced machine learning methods (e.g., random neural network, LSTM, and regression models). Interestingly, the _P.1203_ and _biQPS_ models employ both analytical functions and advanced machine learning in different modules of the models.