Q-FDBA: improving QoE fairness for video streaming

Jiang, Jingyan; Hu, Liang; Hao, Pingting; Sun, Rui; Hu, Jiejun; Li, Hongtu

doi:10.1007/s11042-017-4917-1

Cited by 30 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following the same approach of QoE-fairness and QoEpersonalized control, an SDN-based multi-client bandwidth management architecture for HTTP adaptive video streaming that can support up to 75% users at the same QoE level is proposed in [206], while a Q-learning-based dynamic bandwidth allocation strategy to achieve QoE fairness is given in [207]. A user-level fairness model, UFair, which orchestrates network resource allocation between HAS streams to mitigate QoE fluctuations and improve the overall QoE fairness is given in [193].…”

Section: B Qoe-fairness and Personalized Qoe-centric Control In Sdnmentioning

confidence: 99%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

160

View full text Add to dashboard Cite

The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers' needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a highlevel description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoEoriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

show abstract

Section: B Qoe-fairness and Personalized Qoe-centric Control In Sdnmentioning

confidence: 99%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

160

View full text Add to dashboard Cite

show abstract

“…The same authors in [29] propose a SDN-based QoEaware bandwidth broker for HTTP adaptive streams in an hybrid fiber coax (HFC) network, so-called Bandwidth Management Solution -BMS that dynamically chooses the respective bandwidth allocation decisions and the optimal joint representation in oder to meet the per-session and per-group QoE objectives. J. Jiang et al In [30], a network-driven video streaming architecture is built to design robust mechanisms for multiple players so as to achieve enduser QoE fairness. The SDN controller observes the environment state, computing the reward, and updating Q-value and taking the bandwidth allocation action by forwarding flow tables.…”

Section: Related Workmentioning

confidence: 99%

QoE-aware Bandwidth Allocation for Multiple Video Streaming Players over HTTP and SDN

Thinh¹,

Huyền²,

Quang³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

For many years, the most popular technique for Internet video streaming is hypertext transfer protocol-based adaptive streaming, known as HAS (HTTP Adaptive Streaming). However, a seamless viewing experience can not be just simply guaranteed by HAS only. In the management network, the adaptation of HAS copes with a huge challenge since clientdriven schemes lead to unfair share of available bandwidth when multiple players request adaptive bitrates (i.e bandwidth) through a bottleneck network link. Each client's requesting to maximize its needed bandwidth leads to the competition of network resources. This causes great QoE (Quality of Experience) reduction in terms of main metrics for each player: fairness, efficiency, and stability. In this paper, we propose an integration scheme of bitrate adaptation and Software Defined Networking-based resource allocation that can improve the QoE of competing clients. Our experiments show that the proposed scheme increases at least 20% up to 124% in terms of QoE scores compared with some existing methods as well as gains smoother viewing experience than the solutions of the traditional Internet.

show abstract

“…In this context, it is worth noting that RL [165][166][167][168][169][170][171][172][173][174][175][176][177] was widely applied in SDN paradigm for routing and adaptive video streaming.…”

Section: Rl In Sdnmentioning

confidence: 99%

“…The experimental study showed that their system was able to increase the video stability and achieve better QoE fairness and network resource utilisation. Jiang et al [171] proposed, Q-FDBA, an on-line Q-learning-based dynamic bandwidth allocation algorithm for better QoE fairness. Q-FDBA showed better results when compared to bandwidth-aware streaming and QoE fairness framework.…”

Section: Rl In Sdnmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

View full text Add to dashboard Cite

Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

show abstract

Q-FDBA: improving QoE fairness for video streaming

Cited by 30 publications

References 21 publications

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

QoE-aware Bandwidth Allocation for Multiple Video Streaming Players over HTTP and SDN

Artificial intelligence enabled software‐defined networking: a comprehensive overview

Contact Info

Product

Resources

About