Network-based dynamic prioritization of HTTP adaptive streams to avoid video freezes

Abstract

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. Current heuristics under-perform when sudden bandwidth drops occur, therefore leading to freezes in the video play-out, the main factor influencing users' Quality of Experience (QoE). In this article, we propose an Openflowbased framework capable of increasing clients' QoE by reducing video freezes. An Openflow-controller is in charge of introducing prioritized delivery of HAS segments, based on feedback collected from both the network nodes and the clients. To reduce the sideeffects introduced by prioritization on the bandwidth estimation of the clients, we introduce a novel mechanism to inform the clients about the prioritization status of the downloaded segments without introducing overhead into the network. This information is then used to correct the estimated bandwidth in case of prioritized delivery. By evaluating this novel approach through emulation, under varying network conditions and in several multi-client scenarios, we show how the proposed approach can reduce freezes up to 75% compared to state-of-the-art heuristics.

QoE-Centric Management of Advanced Multimedia Services

“…The QoE is a metric in the same range of the Mean Opinion Score and can be computed as described by Claeys et al [7]. In the evaluated bandwidth scenario, we were able to show Multi-client solution [10] (b) Fig. 2: Obtained results using the proposed in-network-based approach.…”

“…that our multi-client HAS framework resulted in a better video quality and in a remarkable improvement of fairness, up to 60% and 48% in the 10 clients case, compared to MSS and the Q-Learning-based client, respectively. In Figure 2b, a comparison in terms of QoE and freeze time is provided with the MSS, the QoE-RAHAS heuristic [9] and our multi-client solution [10], in a scenario with 30 clients streaming video at the same time. By evaluating our solution under varying network conditions and in several multi-client scenarios, we showed how the proposed approach can reduce freezes up to 75% when compared to benchmark heuristics.…”

Quantitative comparison of application–network interaction: a case study of adaptive video streaming

“…The PM then calculates appropriate media bitrate for each client and directs the clients which media representation to choose. Another PCT proposal for DASH services, which reduces video freezes (or stallings), is presented in [8]. There, network devices are regularly polled for traffic statistics, while clients report on application metrics such as buffer state.…”

“…Control actions, on the other hand, can be employed so as to adapt network behavior, application behavior, or both. For the network level, mechanisms such as bandwidth allocation (e.g., [4,5]), routing reconfiguration (e.g., [6,7]), and traffic prioritization (e.g., [8]) can be applied, which we refer to as network control. At the application level, for example, a client can request from a server another media content resolution and bitrate (e.g., for video) in response to bandwidth fluctuations, or adapt its playout delay (e.g., for audio) so as to alleviate consequences of network jitter, which will collectively be referred to as application control.…”

“…So as to extend this notion in a way to include the solutions that aim at establishing a cooperative control interplay between applications and the network, we introduce the term Application-Network Interaction, or App-Net for short. With this in mind, several App-Net solutions also propose a coordinated approach for the QoE management (e.g., [5,8]), which assumes a logically centralized entity collecting the application and network monitoring information, and then deciding on control actions between these two levels. In this work, we refer to such an entity as the policy manager (PM).…”