Software‐defined network‐based prioritization to avoid video freezes in HTTP adaptive streaming

Abstract

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 playout, the main factor influencing users' quality of experience (QoE). This issue is aggravated in case of live events, where the client-side buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In this article, we propose a framework capable of increasing the QoE of HAS clients by reducing video freezes. The framework is based on OpenFlow, a widely adopted protocol to implement the softwaredefined networking principle. An OpenFlow controller is in charge of introducing prioritized delivery of HAS segments, based on the network conditions and the HAS clients' status. Particularly, the HAS clients' status is obtained without any explicit clients-to-controller communication, and thus, no extra signaling is introduced into the network. Moreover, this OpenFlow controller is transparent to the quality decision process of the clients, as it assists the delivery of the segments, but it does not determine the actual quality to be requested. In order to provide a comprehensive analysis of the proposed approach, we investigate the performance of the proposed OpenFlow-based framework in the presence of realistic Internet cross-traffic. Particularly, we model two types of applications, namely, HTTP web browsing and progressive download video streaming, which currently 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 freeze time for the HAS clients due to network congestion up to 10 times compared with stateof-the-art heuristics, without impacting the performance of the cross-traffic applications. SOFTWARE-DEFINED NETWORK-BASED PRIORITIZATION TO AVOID VIDEO FREEZES IN HAS 249 the available resources. Such dynamic adaptation results in a smooth video streaming experience. Nevertheless, several inefficiencies still have to be solved in order to further improve users' quality of experience (QoE). As reported by Akshabi et al. and Riiser et al., current rate adaptation heuristics perform quality selection sub-optimally, especially when a sudden bandwidth drop occurs [1,2]. This leads to unnecessary quality switches and video playout interruptions, which negatively affect the final QoE of the users. Similar conclusions are drawn in the 2015 Conviva report on HAS [3]. The report reveals that almost 29% of the analyzed HAS sessions exhibit at least one video freeze. This problem is mainly due to the unmanaged nature of the current HAS technologi...

A machine learning-based framework for preventing video freezes in HTTP adaptive streaming

“…Moreover, it is more robust in case of fault or malfunctioning of the network equipment, as the clients can continue to operate (at a sub-optimal level) without the in-network system. A network-based prioritization framework was already presented in our previous work [5], where we analyzed the prioritization system in terms of dimensioning of the prioritization queue, scalability issues and its performance compared with several purely client-based solutions. Even though the presented approach showed excellent results, it is affected by two main drawbacks.…”

“…This communication is carried out by using in-network signaling instead of a direct communication channel between the controller and the clients [5]. More specifically, the prioritization switch can be configured to mark prioritized packets with a specific Differentiated Services Code Point (DSCP) field.…”

“…The freeze predictor does not require any a priori knowledge on the characteristics of the video, as the segment duration or the bit-rates of the different quality levels, nor on the client's configuration, in terms of maximum buffer size and start-up buffering time. Although this information can enhance the performance of the controller's logic as shown in our previous work [5], it is difficult to obtain in a real environment. By properly training the freeze predictor, it is still possible to reduce the number of video freezes experienced by the clients, without making any assumption on the streamed videos and the clients themselves.…”

Buffest

“…Finally, it should be noted that many techniques have been proposed for client-driven or server-assisted quality adaptation [4,24], and for network-assisted prioritization of lows [6,26] using SDN-based technologies such as OpenFlow [33]. Others have proposed network-assisted quality selection for HAS clients based on network-based monitoring [8], or used measurements to model and characterize user satisfaction when using online services [38].…”

Live streaming of 4K ultra-high definition video over the internet

“…We refer to our previous works by Huysegems et al and Petrangeli et al for a more extensive view on the matter [7,9].…”