# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 4 Prediction of Network Throughput
### Discussion of the Results  
Our analysis shows that an LSTM ANN obtains better prediction accuracy compared with a naive predictor based on a moving average. The prediction achieved the best results for traffic registered by fixed terminals. The traffic acquired from mobile equipment, especially that operating on a 5G network, had a more irregular structure and was harder to predict for both the LSTM ANN and a moving average. However, for mobile traces, the accuracy of the prediction made by the moving average deteriorates even faster than the accuracy of an LSTM ANN. From the two LSTM versions we tested, the BdLSTM shows slightly better prediction accuracy compared to the unidirectional LSTM. Although we used an NRMSE (7), comparison with other research is not easy, as their NRMSE values differ significantly across different works. For instance, in [9], the NRMSE ranges between about 0.1 and 5.5 depending on an LSTM ANN and a data set used for the prediction, whereas in [12], the NRMSE is below 0.1 for all experiments. It is also unclear how the authors normalized their errors as they did not specify explicit mathematical formulas in their works. In other mentioned works in Section 2, e.g., [6; 7; 8; 10], the authors provided non-normalized and non-comparative mean square error measures of prediction errors. Sometimes this measure is normalized as in [34] (normalized mean absolute error) or [13] (relative MSE) but is non-comparative with NRMSE without additional data.  
Exemplary DASH algorithms supported by throughput prediction achieved better performance than their versions with default throughput estimators. Most gained the MSS and ExoPlayer playback strategies, while the results for Tian were lower. An explanation is the degree of complexity of the examined approaches. The first two algorithms rely heavily on rate estimation and use for this purpose moving averages, while the buffer measurements play a supportive role. In the case of Tian, the algorithm uses primarily the buffered video time as a feedback signal, while the expected throughput is estimated using support vector regression. This shift of focus results in a lower impact of prediction on the algorithm performance.  
To our best knowledge, there is only a single work with which we can directly compare the results because every research uses its own set of measures. In our work, the quality improvement is between 5% and 25%, while for the perfect throughput prediction, [PERSON] et al. [35] reached 55% using a similar nQoE (4) measure. The authors built a perfect estimator by looking explicitly into the future values of time series and applied it to three algorithms: ExoPlayer, Elastic [36] and Arbiter+ [37]. Similarly to our case, the simpler  
Figure 13: The relative improvement of the nQoE metric across different HAS algorithms and network traces with respect to the no-prediction: (**a**) MSS, LSTM, (**b**) ExoPlayer, LSTM, (**c**) Tian, LSTM, (**d**) MSS, BdLSTM, (**e**) ExoPlayer, BdLSTM, and (**f**) Tian, BdLSTM. The nQoE metrics are normalized to the no-prediction case set to 0% improvement. The scenarios with traces from the mobile environment obtain, on average, better results, but their variation is also higher.  
algorithm ExoPlayer profited the most from the prediction. The prediction gave a less impressive nQoE increase for the two other, more complex algorithms.