# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 4 Results
### QoE Quantization Performance  
In Tables 5 and 6, the central subfigures present the scores of the labeled and predicted QoE scores of the different network services in the test scenario, and the traffic model hyperparameters are also presented. Meanwhile, the central subfigures also provide an insight into the absolute prediction errors for test scenarios. Additionally, normalized root-mean-squared error (NRMSE) is used to measure error between the model predicted MOS score and the ground truth MOS score, NRMSE is calculated by  
\[NRMSE=\frac{1}{N}\sqrt{\sum\limits_{i}^{N}\left(QoE_{p}-QoE_{T} \right)^{2}} \tag{15}\]  
where \(N\) denotes the number of test samples, \(QoE_{p}\) and \(QoE_{T}\) are the model predicted QoE and the ground truth QoE value, respectively. In Table 7, we compare the NRMSE of our model with the other five works. It can be seen that among the 11 types of traffics, the model we propose achieves the seven smallest values of NRMSE, whereas the difference of the other four NRMSE with minimum values are not large (0.1581 vs. 0.1327 [28] in file sharing, 0.1768 vs. 0.1699 [29] in live video, 0.1803 vs. 0.1722 [24] in live audio, 0.2151 vs. 0.1856 [24] in meeting video).  
Further, one may notice that the NRMSEs are lower in on-demand multimedia services than those in live multimedia, online games, and meeting video services. It can be interpreted that, nowadays, on-demand steaming services have a much higher proportion than live streaming services, and people are familiar with them. Therefore, users' will have consistent senses toward on-demand streaming services, which allows a higher quantization accuracy with the mathematical model. In addition, to improve the accuracy of MOS prediction, we should ensure that the QoS metric range is fully covered by the labeled dataset and cross validation methods can be applied to check the MOS inconsistency caused by individual's subjective fluctuations.  
Figure 3: Traffic classification performance comparison between our applied random forest method and other classifiers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Service Type** & **Labeled QoE vs. Predicted QoE** & **Parameter Values** \\ \hline \hline \multirow{4}{*}{on-demand audio} & \multirow{4}{*}{\(\alpha=1.1466\), \(\beta=0.0299\), \(\gamma=-0.3209\), \(a=7.6178\), \(b=0.5831\), \(c=-15.2617\), \(d=0.6199\), \(p=-12.1927\), \(q=-0.0103\)} \\ \cline{2-3}  & & \(\alpha=0.5003\), \(\beta=13.0852\), \(\gamma=-0.0488\), \(a=0.0136\), \(b=0.9295\), \(c=-0.0420\), \(d=0.4487\), \(p=-0.0182\), \(q=-0.0703\) \\ \cline{2-3}  & & \(\alpha=0.2375\), \(\beta=1.2047\), \(\gamma=2.0718\), \(a=0.3321\), \(b=1.2422\), \(c=-0.4628\), \(d=0.3522\), \(p=-0.0937\), \(q=-1.0115\) \\ \cline{2-3}  & & \(\alpha=1.4757\), \(\beta=0.4038\), \(\gamma=2.0749\), \(a=0.5913\), \(b=1.1134\), \(c=-1.2989\), \(d=0.4902\), \(p=-0.3592\), \(q=0.4521\) \\ \cline{2-3}  & & \(\alpha=0.8771\), \(\beta=1.9647\), \(\gamma=0.5635\), \(a=0.2743\), \(b=0.9683\), \(c=-0.0212\), \(d=-0.8657\), \(p=-0.0033\), \(q=0.0893\) \\ \cline{2-3}  & & \(\alpha=1.6466\), \(\beta=0.0293\), \(\gamma=0.1270\), \(a=4.3215\), \(b=0.7696\), \(c=-14.1519\), \(d=0.4361\), \(p=-8.6823\), \(q=0.0838\) \\ \cline{2-3}  & & \(\alpha=1.0901\), \(\beta=0.2868\), \(\gamma=1.5876\), \(a=0.7781\), \(b=1.0480\), \(c=-1.9215\), \(d=0.4574\), \(p=-0.1893\), \(q=0.5047\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Flow characteristics of different network service types. For each type of network service, we selected three APP packet streams to demonstrate the flow distribution over time.