# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 2 Related Works
Except for the historical throughput, an LSTM ANN sometimes operates on additional data to improve the results; for example, the authors of [12] used a complex LSTM ANN with five hidden layers and a multivariate dataset containing information about all downlink and uplink traffic for a given base station. Other works use a meta-learning scheme, including a set of predictors, each optimized to predict a particular kind of traffic. The study by [PERSON] et al. [9] applied this technique to improve traffic scheduling in base stations with a prediction horizon set to 100 ms. The authors of [13] stated that, for the prediction of flow throughput, the best result was achieved by a random forest technique, which outperformed a deep ANN. They tested different time windows spanning from 0.03s to 4s on nearly 1 million flows containing traces from miscellaneous networks and applications. In addition to flow history data, the study used other attributes related to TCP: window size, window scale or segments retransmissions. [PERSON] et al. in [14] analyzed the effect of different parameters of an LSTM ANN and ARIMA on prediction accuracy. The simulation demonstrated the superiority of LSTM over ARIMA, especially for longer time series. The paper [15] employs the same methods and concludes that historical traffic is the most important feature when predicting future network throughput. However, the authors also state that relying only on the throughput alone leads to worse performance compared to the methods using more inputs. Another short-term prediction is proposed in [16], where [PERSON] et al. evaluated the ARIMA and LSTM for the prediction of both single and multiple flows taking into account video and non-video traffic. Using a prediction span between 10 and 100 ms, they found that LSTM ANNs achieved good results at the cost of more training computations. DASH algorithms usually implement a simple throughput prediction based on an average of past measurements. Advanced solutions try to optimize video playback by employing more elaborate prediction techniques. Some researchers state that the playback algorithms fed with an accurate estimation of network throughput outperform those that do not apply predictive methods, while others observe that prediction alone is not sufficient and should be combined with other techniques, e.g., buffer control [1]. In [17], the authors focus on inaccuracies in throughput measurements occurring due to idle periods between the chunks causing sub-optimal adaptation decisions. The authors show that a simple prediction scheme based on adaptive filtering can improve the quality of the streaming video. [PERSON] et al. in [18] compared several prediction schemes, among them support vector machines, random forest and LSTM ANN, and applied them to a single DASH algorithm. Interestingly, the authors reported that a DASH player supported by the best predictor achieved nearly the same performance as in the case of the ideal prediction. In addition to application data, the model relied on network data, reaching as deep as the physical layer, which may be a reason behind the extraordinary high accuracy of the prediction. To improve the video quality, in [19], the authors propose active queue management (AQM). The AQM uses LSTM to predict the router's queue delay, which temporarily stores DASH packets. Increasing queue delay triggers removal of the packets in the queue. This, in turn, forces traffic sources to reduce their transmission rate and induces a video player to decrease the video-bit rate before the network stalls, which allows for smoother video playback. In [20], we proposed employing (F)ARIMA processes, shallow ANNs and an recursive auto-encoder to enhance the performance of a DASH system. In this work, we operate on traces gathered from 4G and 5G networks. To date, the analysis of the traffic generated by the latter network has limited coverage in the literature.