ML based QoE enhancement in SDN context: Video streaming case
Asma Ben Letaïfa, Maher Gzam, Skhiri Mouna · 2017
In today's world, video streaming rose above all other types of traffic. In fact, providing this service with a high quality presents the most challenging task among the advancements in networking technologies. Researchers are trying to help creating a more efficient network where congestion, broadband limitations and skyrocketing number of users present ever-diminishing obstacles. When it comes to us, we present in this paper a machine learning approach combined with adaptive coding in order to provide a better QoE for video streaming services. This solution will be established using SDN architecture. We can justify this choice because we need a centralized architecture, where the totality of the network is known, to predict its status. So, we will implement a machine learning algorithm in the controller: this algorithm, called ML based SSIM, will calculate approximately the quality needed for a video to be streamed. Finally, the quality found by the ML-based SSIM Algorithm will be combined with the network situation to choose the right coding. First part of the paper deals with a brief introduction of SDN networks, QoE requirement and ML algorithms. Secondly, we expose the SSIM approach and explain how our proposed one is based on. The last part of the paper deals with experiments: we describe SDN environment deployment, describe scenarios and give at the end results and values. We highlight at the end the future of our proposition.