# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 5 Conclusions  
In this work, we improved the performance of adaptive streaming algorithms employing LSTM ANNs that predicted network throughput. As a result, the system provides higher video quality without longer buffering times or a higher frequency of bit rate changes. The improvement is more significant in 5G networks; nevertheless, advanced playback algorithms gain less from throughput prediction. The best results achieved relatively simple strategies based on throughput and buffer measurement. The improvement is not uniform across the usage scenario and depends on network conditions. The prediction gain was smaller for a more complex playback algorithm supported by the proportional-integral-derivative controller. From the two tested LSTM versions, the bidirectional LSTM gained a slightly better prediction score.  
Our approach does not require modifications of the network infrastructure or the TCP stack. Unlike proprietary solutions, DASH has an open specification and leaves the implementation of the playback logic to third parties; thus, our solution can be integrated with adaptation algorithms. The prediction model relies only on past throughput measurement and works without additional network information, which can be hard to acquire. Therefore, the prediction accuracy and the quality improvement are less impressive than in some cited works, which support their prediction with additional data.  
We identified several potential paths for further investigation. Firstly, the traffic models can be improved, as some authors report that the best results are usually obtained by hybrid techniques. It will be interesting to investigate how to leverage the additional information to increase the prediction accuracy, as more elaborate approaches may better adjust to network data and provide better input for adaptive algorithms. Secondly, there is room to extend our prediction beyond the current single video chunk horizon. Thirdly, one can investigate the optimal length of the training, test sets and a time horizon that provides the best prediction. Finally, we would like to move from the offline analysis to an online prediction made by a mobile device, which measures the throughput in real-time and provides information about battery, memory and processor usage.  
The work was carried out within the statutory research project of the Department of Computer Networks and Systems.  
Not applicable.  
[1]**Data Availability Statement:** Publicly available datasets were analyzed in this study. The data can be found here [[https://www.ucc.ie/en/misl//research/datasets/ivid_4g_lte_dataset/](https://www.ucc.ie/en/misl//research/datasets/ivid_4g_lte_dataset/)]([https://www.ucc.ie/en/misl//research/datasets/ivid_4g_lte_dataset/](https://www.ucc.ie/en/misl//research/datasets/ivid_4g_lte_dataset/)) (accessed on 5 October 2022) and here [[https://github.com/uccmisl/5](https://github.com/uccmisl/5) Gdataset]([https://github.com/uccmisl/5](https://github.com/uccmisl/5) Gdataset) (accessed on 5 October 2022).  
[2]**Conflicts of Interest:** The authors declare no conflict of interest.