ABR prediction using supervised learning algorithms

With the massive increase of video traffic over the internet, HTTP adaptive streaming has now become the main technique for infotainment content delivery. In this context, many bandwidth adaptation algorithms have emerged, each aiming to improve the user QoE using different session information e.g. TCP throughput, buffer occupancy, download time... Notwithstanding the difference in their implementation, they mostly use the same inputs to adapt to the varying conditions of the media session. In this paper, we show that it is possible to predict the bitrate decision of any ABR algorithm, thanks to machine learning techniques, and supervised classification in particular. This approach has the benefit of being generic, hence it does not require any knowledge about the player ABR algorithm itself, but assumes that whatever the logic behind, it will use a common set of input features. Then, using machine learning feature selection, it is possible to predict the relevant features and then train the model over real observation. We test our approach using simulations on well-known ABR algorithms, then we verify the results on commercial closed-source players, using different VoD and Live realistic data sets. The results show that both Random Forest and Gradient Boosting achieve a very high prediction accuracy among other ML-classifier.