# A Telepresence Wheelchair with 360-Degree Vision Using WebRTC
## 3 Results
### Experiment 2: QoE Estimation of Wireless Video Streaming Using Neural Networks  
In this experiment, we developed a feed-forward neural network which consisted of multiple layers of neurons followed by an output layer of linear neurons. The model consists of an input layer, hidden layers, and an output layer. The output layer is of the Purelin type which covers the entire MOS range. The modeling scheme chosen is a feed-forward with a two-layer neural network. This gives the model the ability to approximate both linear and non-linear data functions. One hidden layer was used to perform the computation of weights and biases with considerations of the trade-off between performance and accuracy. It is known that the greater the number of hidden layers, the greater is the time taken by the model to compute the weights and bias. However, the weights and bias model the function very accurately. The tansig function (covers range -1 to 1) is used to facilitate accurate modeling of non-linear aspects of the modeled data. The linear function is used at the output layer such that the output can take the entire MOS values. In the training phase, it is important to define the number of epochs which is the number of sweeps through all the records in the training set. To have model accuracy, multiple sweeps are required. The number of epochs affects both the performance and accuracy of the model. With a large number of epochs, the model gets overtrained with given input set and does not correspond well to small fluctuations of input data. With a small number of epochs, model accuracy is reduced. The type of training function used is the Trainlm (Levenberg-Marquardt), which is suitable in terms of accuracy for the size of the network in our problem. It cuts off the model training (number of epoch) before the model becomes overtrained with a given data set and learns much faster than the normal train function training. The neural network toolbox available in MATLAB is used for developing our QoE model estimation. The proposed neural network (NN) is presented in Figure 11. The parameters to be modeled are network jitter, round-trip-time, packet loss, and bit rate. The resulting output is the mean opinion score ranking with range (1-5). We collected the set of 300 cases in which MOS rankings are in compliance with the ITU-T standard. The NN was trained by using 100 cases while the other 100 cases were used for the NN validation, and the remaining 100 cases were used to test the NN. **Results:** Figure 12 indicates the performance progress for training, validation and test over epochs. It is worth noting that the number of epochs is the number of sweeps through all the data records in the training set. The number of epochs is an important factor which determines the performance and  
Figure 11: The proposed neural network for QoE estimation with weights w and bias b. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resolution** & **Average Encoding** & **Average Delay** & **Average Data** & \\
**(Pixels)** & **Time (ms)** & **(ms)** & **Throughput (kbps)** & **Packet Loss (\%)** \\ \hline \(320\times 240\) & \(4.00\pm 0.01\) & \(8.06\pm 0.03\) & 1641.635 & 0.10 \\ \(640\times 480\) & \(7.00\pm 0.05\) & \(12.08\pm 0.16\) & 1617.374 & 0.36 \\ \(1024\times 576\) & \(11.00\pm 0.02\) & \(18.01\pm 0.23\) & 1582.916 & 1.07 \\ \(1280\times 720\) & \(11.46\pm 0.06\) & \(18.73\pm 0.18\) & 1492.122 & 1.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average video streaming characteristics for different video resolutions. accuracy of the NN model. It can be observed from Figure 10 that the best number of epochs is 12 where the validation performance reached a minimum. To validate the neural network, we provide a regression plot in Figure 12, which reveals the relationship between the MOS output of the NN and the MOS target. As can be seen in Figure 12, the training data shows a good fit while validation and test results indicate R values that greater than 0.9.