Quality estimation for DASH clients by using Deep Recurrent Neural Networks

Dynamic Adaptive Streaming over HTTP (DASH) is a technology designed to deliver video to the end-users in the most efficient way possible by providing the users to adapt their quality during streaming. In DASH architecture, the original content encoded into video streams in different qualities. As a protocol running over HTTP, the caches play an important role in DASH environment. Utilizing the cache capacity in these systems is an important problem where there are more than one encoded video files generated for each video content. In this paper, we propose a caching approach for DASH systems by predicting the future qualities of DASH clients. For the prediction, we use learning model, and the qualities that will be cached are determined by using this model. The learning model is designed using Recurrent Neural Networks (RNNs) and also Long Short Term Memory (LSTM) which is a special type of RNNs with default behavior of remembering information for long periods of time. We also utilize SDN technology to get some of the outputs for the learning algorithm. The simulation results show that predicting future qualities helps to reduce the underruns of the clients when cache storage is utilized.