Design and experimental evaluation of network-assisted strategies for HTTP adaptive streaming

Cofano, Giuseppe; Cicco, Luca De; Zinner, Thomas; Nguyen-Ngoc, Anh Vu; Tran-Gia, Phuoc; Mascolo, Saverio

doi:10.1145/2910017.2910597

Cited by 81 publications

(88 citation statements)

References 22 publications

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“…In general, approaches quantifying fairness have relied mainly on measures such as second-order statistics (variance, standard deviation, coefficient of variation), entropy-based measures, and difference to optimal solution, e.g. [6]. A key issue is defining what is considered to be "fair", and then designing and evaluating various scheduling policies in terms of fairness.…”

Section: Notion Of Fairness In Shared Environmentsmentioning

confidence: 99%

A new QoE fairness index for QoE management

et al. 2018

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The user-centric management of networks and services focuses on the Quality of Experience (QoE) as perceived by the end user. In general, the goal is to maximize (or at least ensure an acceptable) QoE, while ensuring fairness among users, e.g., in terms of resource allocation and scheduling in shared systems. A problem arising in this context is that the notions of fairness commonly applied in the QoS domain do not translate well to the QoE domain. We have recently proposed a QoE fairness index F, which solves these issues. In this paper, we provide a detailed rationale for it, along with a thorough comparison of the proposed index and its properties against the most widely used QoS fairness indices, showing its advantages. We furthermore explore the potential uses of the index, in the context of QoE management and describe future research lines on this topic.

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Section: Notion Of Fairness In Shared Environmentsmentioning

confidence: 99%

A new QoE fairness index for QoE management

et al. 2018

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“…A NOVA algorithm then computes bandwidth slices for each DASH client so that several QoE-IFs are optimized, the respective network controller performs the bandwidth slice allocation, while rate adaption is performed by the clients independently. Cofano et al [5] describe several NCT strategies that target QoE fairness among different clients. The bandwidth reservation strategy uses application-level information so as to assign media flows to dedicated queues on network interfaces, while a hybrid strategy combines bandwidth reservation with media bitrate calculation (similarly to [4]).…”

Section: App-net Mechanisms For Improving Qoementioning

confidence: 99%

“…The second approach, Network Assisted DASH Fairness Enhancement (NADE), is proposed by Cofano et al [5]. Similarly to QoE-FF, this strategy targets the video quality fairness among heterogeneous clients in a shared system.…”

Section: Network Assisted Dash Fairness Enhancementmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

2017

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Managing quality of experience (QoE) is now widely accepted as a critical objective for multimedia applications and the supporting communication systems. In general, QoE management encompasses: (1) monitoring of the key influence factors and QoE indicators, and (2) deciding on the appropriate control actions as specified by the management goal. Many multimedia applications, e.g., video streaming and audio conferencing, are able to adjust their operational parameters so as to react to variations in the network performance. However, such an adaptation feature is mostly based on a local client view of the network conditions, which may lead to an unfair allocation of network resources among heterogeneous clients and, thus, an unfair QoE distribution. In order to tackle this issue, there is the call for a cooperation between the applications and the underlying network, which includes applicationnetwork interaction (App-Net) in terms of: (1) exchanging information on the monitored QoE indicators, and (2) coordinating the QoE control actions. Various App-Net mechanisms focusing on specific use cases and applications have been proposed to date. This paper gives an overview of App-Net mechanisms and proposes a generic App-Net model that provides the means to realize a coordinated QoE-centric management. Based on the App-Net model, we develop an evaluation methodology to compare three App-Net mechanisms for managing QoE of HTTP adaptive streaming (HAS) against a baseline HAS service. The aim of this quantitative comparison is to explore the trade-offs between QoE gains and the complexity of App-Net implementation, with respect to the number of monitoring and control messages, achieved video quality, and QoE fairness among heterogeneous clients. Our ultimate goal is to set up reproducible experiments that facilitate a holistic evaluation of different App-Net mechanisms.

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