# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 2 Related Works  
The literature on network throughput prediction is extensive and usually focuses on the Internet traffic, which aggregates flows generated by a whole spectrum of applications. The authors of [2] show a comprehensive review of prediction methods and compare some of them. A number of research studies used linear regression, autoregressive integrated moving average (ARIMA), ARIMA with an explanatory variable (ARIMAX), multiple linear regression, support vector regression, GARCH processes and many other techniques derived from statistics. These methods are conventionally called traditional and are considered descriptive in nature, aimed at analyzing datasets with finite, countable, and explainable predictors. The authors of [3] made a comprehensive comparison of the traditional methods using a 3G/HSPA data set. The newer prediction schemes based on machine learning are sometimes used in accordance or opposition to the traditional methods, and there is an ongoing debate about their accuracy and computational requirements [4]. ANNs, considered a non-traditional approach, play a role in network traffic prediction mainly due to their universality and accuracy. Historically, the popularity gained simple ANNs with a single hidden layer because versions with multiple layers, called deep ANNs, were harder to train. After researchers overcame the training issues, deep ANNs became popular and gradually replaced single-layered ANNs as a tool for the solution of more complex problems. Because deep ANNs added more complexity and nonliterary to the network traffic model, their prediction accuracy increased. [PERSON]'s comparative study [5] discusses both approaches in the context of network traffic prediction. From the variety of deep ANNs, LSTM ANNs are one of the possible choices and are applied readily due to their capability to capture long-range dependencies characteristic of aggregated network traffic, i.e., traffic composed of multiple flows. For example, an LSTM ANN was used to identify recurrent patterns of various metrics to predict traffic in cellular networks in [6]. The work focuses on long-horizon prediction counted in days. In [7], the authors enhanced their LSTM-based prediction with an autocorrelation function, which captured the traffic cycles on a daily scale with a prediction span between 5 and 60 min. In [8], the authors proposed to remove extreme values by scaling a traffic trace. Their LSTM ANN had four hidden layers with a time window set to 32 units or more. The authors did not provide a prediction horizon; however, the data granularity was more than one minute. In the case of multimedia applications, the prediction of a single traffic flow is more important and challenging than aggregated traffic [9]. A single flow, especially if transmitted through an unstable mobile environment, can have much variability and sudden changes compared with an aggregated trace smoothed by statistical multiplexing. Hence, the models of the aggregated traffic are usually not suitable for describing a single flow generated by a DASH system streaming video segments in an ON-OFF fashion, which contributes to significant bursts in the transmission. As single-flow prediction is a more challenging task, there are fewer works dedicated to this topic, and their authors focus mainly on a short-time prediction span. In [10], the authors applied LSTM and gated recurrent units (GRU) ANNs to predict traffic flow obtained from a sensor network for a five-minute time horizon. They achieved better results compared with the ARIMA processes. A comparative study [11] showed that for cellular network traffic, LSTM provides better prediction than a stacked autoencoder. Unfortunately, the authors did not provide information about the traffic traces used in the experiment.