# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 3 Proposed Method
### QoE Quantization Model  
Here, we capture ground truth of \(<QoS\)\(metrics:QoE>\) data to estimate the coefficients of the QoE quantization model (such as Equations (5) and (7)) of different services, which can be described as a QoE model estimation experiment taking the following steps.  
1. Connect user terminal devices to the wireless network (the Wi-Fi hotspot).
2. Generate \(\sim\)90-100 different network environments by varying network parameters (\(DL\), \(LR\), and \(TH\)) at the Wi-Fi server terminal, where \(DL\), \(LR\), and \(TH\) denote the packet delay, loss rate, and flow throughput rate, respectively.
3. Collect the QoS data and its corresponding MOS score under different environment, which will then be used as the training/test data and labels.
4. Also, here, we need to divide the QoS data into training subset and test dataset without overlap, so the test dataset can help to check and improve model accuracy.
5. Label the QoS training dataset and estimate the coefficients through machine learning algorithm.
6. Test model accuracy and update the coefficients into a certain extent that meet the practical demand.  
To solve the drawback that mentioned in Section 2.2 of the linear mapping function \(f_{k}\left(\cdot\right)\), we consider the relations between QoS and network parameters separately.  
For the network packet delay, \(DL\),  
\[QoS(DL)=a\cdot exp(b\cdot DL)+\phi_{1} \tag{2}\]  
For the packet loss rate, \(LR\),  
\[QoS(LR)=\frac{c}{LR+d}+\phi_{2} \tag{3}\]  
And for the flow throughput rate, \(TH\),  
\[QoS(TH)=p\cdot log(TH)+\phi_{3} \tag{4}\]  
After merging and combining the three influencing factors, we get the QoS expression as follows,  
\[QoS = QoS(DL)+QoS(LR)+QoS(TH) \tag{5}\] \[= a\cdot exp(b\cdot DL)+\frac{c}{LR+d}+p\cdot log(TH)+q\]  
where \(DL,LR\), and \(TH\) denote the transmission delay, packet loss rate, and data throughput rate, respectively. \(q=\phi_{1}+\phi_{2}+\phi_{3}\) and the parameters \(a,b,c,d,p,q\) need to be determined latter for different  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Category** & **Description** & **Number of Feature** \\ \hline Packets & bidirectional packet number & 2 \\ Bytes & bidirectional Max, Min, Mean, and Var of packet bytes & 8 \\ Inter-Arrival Time & bidirectional Max, Min, Mean, and Var of inter-arrival time & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected features for our proposed random forest-based classification model.  
types of network services. [PERSON] et al. [27] has demonstrated that when the current QoE value is high, a tiny perturbation in QoS can cause a significant fluctuation, whereas when the current QoE value is low, even a significant change pales; the relation can be described with a formula as follows,  
\[\frac{\partial QoE}{\partial QoS}=-(QoE-\gamma) \tag{6}\]  
The solution of the above differential formula is  
\[QoE=\langle\alpha\cdot exp(-\beta\cdot QoS)+\gamma\rangle \tag{7}\]  
where \(\langle\cdot\rangle\) is the rounding function, \(\alpha>0,\beta>0\) also varies in different types of network traffic, i.e., on-demand video, on-demand audio, live video, live audio, etc. Using Equations (5) and (7), we establish the mathematical formulations between the values of QoE and QoS metrics (\(DL,LR,TH\)).