MATURE: Multistage Throughput Prediction for Adaptive Video Streaming in Cellular Networks

Accurate Throughput Prediction (TP) represents a cornerstone for reliable adaptive streaming in challenging mediums, such as cellular networks. Challenged by the highly dynamic wireless medium, recent state-of-The-Art solutions adopt Deep Learning (DL) models to improve TP accuracy. However, these models perform poorly in critical, rare network conditions, leading to degraded user Quality of Experience (QoE). Such performance results from depending solely on the model's capacity and power of learning, without integrating system knowledge into the design. In this paper, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy and user experience. MATURE's operation involves characterising the operating context before estimating the network throughput. Our performance evaluation shows that MATURE improves the average user QoE by 4%-90% in critical network conditions when compared to state-of-The-Art.