Towards network-wide QoE fairness using openflow-assisted adaptive video streaming

Georgopoulos, Panagiotis; Elkhatib, Yehia; Broadbent, Matthew; Mu, Mu; Race, Nicholas

doi:10.1145/2491172.2491181

Cited by 213 publications

(169 citation statements)

References 19 publications

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“…The QoE Fairness Framework [4] is a PCT approach for a fair QoE distribution among heterogeneous DASH clients. It realizes App-Net by monitoring network bandwidth, collecting user device features and MPDs, and reporting them to a PM.…”

Section: App-net Mechanisms For Improving Qoementioning

confidence: 99%

“…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 first approach is called QoE Fairness Framework (QoE-FF) [4], which employs a centralized controller (PM) to govern the video quality selection among heterogeneous HAS clients and to produce a fair QoE distribution. This mechanism is illustrated in Fig.…”

Section: Quality Of Experience Fairness Frameworkmentioning

confidence: 99%

“…However, such an adaptation feature is mostly based on a local client/server view of the network conditions, which may lead to an unfair distribution of the available resources among contending traffic flows and, hence, negatively impact QoE. Even if a fair resource share is achieved, heterogeneous clients may still receive different QoE levels [4]. For instance, a smartphone device and a laptop computer may be assigned an equal network bandwidth for video streaming, although a continuous video reproduction at the smartphone would demand less bandwidth.…”

Section: Introductionmentioning

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%

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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.

show abstract

Section: App-net Mechanisms For Improving Qoementioning

confidence: 99%

Section: App-net Mechanisms For Improving Qoementioning

confidence: 99%

Section: Quality Of Experience Fairness Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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Technologies and Architectures for FutureIPTelevision Services

Sassatelli¹,

Montpetit²

2018

IPTV Delivery Networks

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This chapter introduces the current trends of evolution of IPTV from several perspectives. First, we review the transformation of the users' expectations and the consequent changes brought to the business opportunities for the different stakeholders willing to benefit from the Internet video usage. The second section of this chapter presents the main architectural overhauls of IPTV led by the converged usage of different video modalities. In particular, we discuss how IPTV and Over-The-Top (OTT) video distribution mutually feed each other to draw the future of IPTV systems, and insist on the current and foreseen collaboration between the stakeholders. The third section focuses on three main technical points in video distribution through the Internet, namely (i) the caching strategies which allow the telecommunication operators (telcos) to scale up their Internet video distribution, (ii) the envisioned solutions to steady and integrate the wireless last-hop into the whole delivery system, and (iii) we open up on the daunting technological challenges arising with Virtual Reality (VR), if VR shall be the upcoming revolution of user experience and ultimate evolution of TV.

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QoE Management of Multimedia Services Using Machine Learning in SDN/NFV 5G Networks

2022

Multimedia Streaming in SDN/NFV and 5G Networks

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Towards network-wide QoE fairness using openflow-assisted adaptive video streaming

Cited by 213 publications

References 19 publications

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

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

Technologies and Architectures for FutureIPTelevision Services

QoE Management of Multimedia Services Using Machine Learning in SDN/NFV 5G Networks

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