Software-defined Network-based Prioritization to Avoid Video Freezes in HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. It has been shown that state-of-the-art heuristics perform suboptimal when sudden bandwidth drops occur, therefore leading to freezes in the video play-out, the main factor influencing Quality of Experience (QoE) of the users. In this article, we propose an OpenFlow-based framework capable of increasing the QoE of the HAS clients by reducing video freezes. An Openflow-controller is in charge of introducing prioritized delivery of HAS segments, based on the network conditions and HAS clients status. Particularly, the HAS clients status is obtained without any explicit clients-to-controller communication, and thus no extra signalling is introduced into the network. In order to provide a comprehensive analysis of the proposed approach, we investigate the performance of our OpenFlow-based framework in presence of realistic Internet traffic. Particularly, we model two types of applications, HTTP web browsing and progressive download video streaming, which represent the majority of Internet traffic together with HAS. By evaluating this novel approach through emulation in several multi-client scenarios, we show how the proposed approach can reduce freezes due to network congestion for the HAS clients compared to state-of-the-art heuristics, without impacting the performance of web-browsing and progressive download applications.