Efficient and scalable cloud-assisted SVC video streaming through mesh networks

Li, Suoheng; Zhu, Zuqing; Li, Weiping; Li, Houqiang

doi:10.1109/iccnc.2012.6167564

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…There are some works however, that have mainly focused on QoS-aware video traffic routing [32]- [37]. In fact, these studies have investigated the QoS-aware video flow routing in OpenFlow/SDN-enabled networks.…”

Section: Client-based Adaptation Assisted By Network Elementsmentioning

confidence: 99%

“…These studies do not discuss quality adaptation techniques. In addition, the authors of [37] provide a model to determine the k shortest paths and to then assign these to each k SVC layer. They extend their research in [35], in which they considered additional parameters, such as bandwidth and delay, and also enhance the model by using the max flow algorithm.…”

Section: Client-based Adaptation Assisted By Network Elementsmentioning

confidence: 99%

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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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Section: Client-based Adaptation Assisted By Network Elementsmentioning

confidence: 99%

Section: Client-based Adaptation Assisted By Network Elementsmentioning

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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“…Note that for practical SVC video streaming, the bandwidth requirement can change with time. Therefore, we introduce a bandwidth scaling factor to assign certain redundant bandwidth to the streaming flows and make them more reliable [22].…”

Section: ) Flow Conservation Constraintsmentioning

confidence: 99%

Demonstration of OpenFlow-Controlled Network Orchestration for Adaptive SVC Video Manycast

Xue

Chen

Gong

et al. 2015

IEEE Trans. Multimedia

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Software defined networking (SDN) makes networks programmable and application-aware by decoupling network control and management (NC&M) from data forwarding and leveraging centralized NC&M to facilitate user-customized routing and switching. Inspired by these, this paper investigates how to realize the OpenFlow-controlled (OF-controlled) network orchestration that can facilitate efficient scalable video coding (SVC) streaming to heterogeneous clients. Specifically, we consider real-time SVC streaming and address the situation in which video sources reside in geographically-distributed servers and clients can join and leave the streaming services dynamically. We formulate this as a multi-source multi-destination manycast problem and realize the networking system with an OF-controlled SDN architecture. We first design the OF controller to enable efficient network operations. Then, we focus on solving the multi-source multi-destination SVC video manycast problem and design several algorithms. Initially, an integer linear programming (ILP) model is formulated to obtain the optimal solutions for small-scale problems. Next, we try to make the manycast algorithm suitable for practical implementation, and design two time-efficient heuristics. Simulation results indicate that the heuristics can provide close-to-optimal solutions. Finally, we build an OF network testbed that consists of OF switches, SVC video servers and clients, and perform SVC streaming experiments to demonstrate our design. Experimental results verify that the proposed scheme can allocate bandwidth intelligently and ensure high-quality video streaming. To the best of our knowledge, this is the first work that accomplishes experimental demonstration of OF-controlled network orchestration for adaptive SVC video manycast.Index Terms-Manycast, openflow, scalable video coding (SVC), software-defined networking (SDN), video streaming.

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“…To cope with these problems, there have been efforts for coupling the scalable video coding (SVC) [7] with video streaming service over cloud infrastructures [8], [9]. SVC is the scalable extension of H.264/AVC which supports spatial, temporal and quality scalability.…”

Section: Introductionmentioning

confidence: 99%

On the Impact of Layer-Splitting for Cloud-Based SVC Streaming

Moon

Jung

Lee

et al. 2014

2014 International Conference on Future Internet of Things and Cloud

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Video streaming has dominated traffic usage over the Internet. With the development of heterogeneous mobile devices, various streaming environments with different display sizes and network bandwidths have accelerated video streaming to the masses. To accommodate the increasing demand for video, streaming providers have made efforts to build services based on scalable video coding (SVC) over the cloud infrastructure, which leverages elastic amounts of resources and network bandwidth on the fly. In this paper, we suggest a split-layer SVC-based streaming architecture optimized for a cloud environment. By separately controlling streaming instances with awareness of clients device ratio amongst smartphone, tablet, and PC, we improve resource utilization and save network bandwidth. We also evaluate how our proposed system can improve the total cost for providing a video streaming service on a cloud infrastructure. We carry out extensive experiments to evaluate the performance in multiple real-life scenarios by simulating streaming services on (1) a mobile-client-heavy channel, (2) a PC-client-heavy channel, and (3) a device-balanced channel.

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Efficient and scalable cloud-assisted SVC video streaming through mesh networks

Cited by 9 publications

References 9 publications

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

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

Demonstration of OpenFlow-Controlled Network Orchestration for Adaptive SVC Video Manycast

On the Impact of Layer-Splitting for Cloud-Based SVC Streaming

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