Learning-based approach for layered adaptive video streaming over SDN

Uzakgider, Tuba; Çetinkaya, Cihat; Sayıt, Müge

doi:10.1016/j.comnet.2015.09.027

Cited by 36 publications

(24 citation statements)

References 17 publications

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“…The study shows that the integration of AI techniques within SDN is promising, with several research groups introducing the use of reinforcement routing over SDN-based network. The research in [6] introduces a routing algorithm based on RL. The method determines when is the right time to reroute the traffic in order to minimize the packet loss.…”

Section: Related Workmentioning

confidence: 99%

LearnQoS: A Learning Approach for Optimizing QoS Over Multimedia-Based SDNs

Al-Jawad

Shah

Gemikonakli

et al. 2018

2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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Section: Related Workmentioning

confidence: 99%

LearnQoS: A Learning Approach for Optimizing QoS Over Multimedia-Based SDNs

Al-Jawad

Shah

Gemikonakli

et al. 2018

2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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“…Uzakgider et al [222] propose a learning-based approach that minimizes the packet loss rate, quality changes, and controller cost while adapting the flow routes and video quality. Gangwal et al [223] propose an Efficient Layer Based Routing Algorithm (ELBA) that exploits the dynamic re-routing capability of SDN, to different stream layers of Scalable Video Codec (SVC) video over distinct paths.…”

Section: Qoe-centric Routing Mechanisms Using Sdn/nfvmentioning

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

168

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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.

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“…Their approach first determines the N shortest-path routes and then, based on priorities, sends the video layer using one of the paths. In contrast, the approach in [43] utilizes the optimal paths instead of the shortest ones. However, in [42] and [43], the authors only determine an optimal data path and do not consider fairness in their proposed solution.…”

Section: Network-based Adaptationmentioning

confidence: 99%

“…In contrast, the approach in [43] utilizes the optimal paths instead of the shortest ones. However, in [42] and [43], the authors only determine an optimal data path and do not consider fairness in their proposed solution. By running the algorithm for each incoming client (or request), they find a solution regarding the available resources.…”

Section: Network-based Adaptationmentioning

confidence: 99%

S2VC: An SDN-based framework for maximizing QoE in SVC-based HTTP adaptive streaming

Tashtarian

Varasteh

2018

Computer Networks

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HTTP adaptive streaming (HAS) is quickly becoming the dominant video delivery technique for adaptive streaming over the Internet. Still considered as its primary challenges are determining the optimal rate adaptation and improving both the quality of experience (QoE) and QoE-fairness. Most of the proposed approaches have relied on local information to find a result. However, employing techniques that provide a comprehensive and central view of the network resources can lead to more gains in performance. By leveraging software defined networking (SDN), this paper proposes an SDN-based framework, named S 2 V C, to maximize QoE metrics and QoE-fairness in SVC-based HTTP adaptive streaming. The proposed framework determines both the optimal adaptation and data paths for delivering the requested video files from HTTP-media servers to DASH clients. In fact, by utilizing an SDN controller and its complete view of the network, we introduce an SVC flow optimizer (SFO) application module to determine the optimal solution in a centralized and time slot fashion. In the current approach, we first formulate the problem as a mixed integer linear programming (MILP) optimization model. The MILP is designed in such a way that it applies defined policies, e.g. setting priorities for clients in obtaining video quality. Secondly, we show that this problem is NP-complete and propose an LP-relaxation model to enable S 2 V C framework for performing rate adaptation on a large-scale network. Finally, we conduct experiments by emulating the proposed framework in Mininet, with the usage of Floodlight as the SDN controller. In terms of improving QoE-fairness and QoE metrics, the effectiveness of the proposed framework is validated by a comparison with different approaches.

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Learning-based approach for layered adaptive video streaming over SDN

Cited by 36 publications

References 17 publications

LearnQoS: A Learning Approach for Optimizing QoS Over Multimedia-Based SDNs

LearnQoS: A Learning Approach for Optimizing QoS Over Multimedia-Based SDNs

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

S2VC: An SDN-based framework for maximizing QoE in SVC-based HTTP adaptive streaming

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