# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 1 Introduction  
Highly variable packet delays, packet losses and fluctuating mobile network bandwidth make streaming a video challenging. Although modern mobile networks offer high bandwidth, the nature of wireless transmission--inherent variability in signal strength, interference, noise, and user mobility--results in high throughput variability. To overcome this unstable network environment, content providers encode video into multiple versions at different bit rates and split them into segments. A video player periodically downloads the segments, concatenates them and delivers a continuous video stream to a user. During the playback, the player can request a different version of the segments to adapt the video bit rate to the current network conditions. This video delivery concept is called dynamic adaptive streaming over HTTP (DASH).  
The rate-adaptation algorithm is a significant element of a DASH system because the player has to estimate network conditions, including available throughput to ensure proper quality of experience (QoE). The measurement of network throughput, usually based on the moving average, often differs from real throughput. Because of this difference, streaming algorithms can be too conservative and download a video of much lower quality than the throughput allows, or too aggressive and download content of too high quality in given network conditions. A solution to this problem is to predict the future network throughput. As the video segment is usually several seconds long, the prediction is short-term and does not rely on seasonality. Prediction can improve the efficiency of bit rate selection, which translates to lower startup delay and offers better mid-stream bit rate adaptation. Researchers estimate that the applied prediction achieves between 68% and 89% of the optimal quality. The exact numbers are dependent on the playback algorithms employed in a DASH player [1].  
In our work, we predict throughput using traces from 4G and 5G networks from static and mobile usage scenarios. In the next step, we replace the default methods of throughputestimation in three DASH algorithms with the obtained predicted results. Finally, we show that the DASH algorithms achieve better performance when equipped with prediction compared with reliance only on an average of network measurements. Our approach does not require any modification to network protocols or the DASH standard and its components. The solution only affects users' players where a more elaborate algorithm based on LSTM artificial neural networks (ANNs) replaces the default throughput estimation. With the information provided by the player's measurement module, the algorithm can estimate the expected throughput for the next few seconds. Because an LSTM ANN requires some computation needed to learn and continually update its knowledge, this process increases the usage of memory and CPU of a video player.  
Taking the above into account, the contributions of the paper are: the prediction of 5G network throughput at the application level in static and mobile scenarios; the comparison of the prediction quality with the 4G trace; the usage of stacked uni- and bidirectional LSTM ANNs versions for the prediction, and the application of the prediction made by an LSTM ANN to improve quality of video for three DASH players.  
In the next section, we describe the related works. In Section 3, we present the video streaming model, adaptation strategies of video playback and video quality measurement. Section 4 shows the prediction model, LSTM ANNs, used for the prediction, data characteristics, prediction results and the prediction influence on the video quality. The last section concludes the paper.