分布式虚拟选择性转发单元和 SDN 辅助边缘计算优化多方 WebRTC 视频会议

IF 3.4 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
R. Arda Kırmızıoğlu , A. Murat Tekalp , Burak Görkemli
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引用次数: 0

摘要

网络服务提供商(NSP)对通过部署软件定义网络(SDN)和网络功能虚拟化基础设施在网络边缘部署网络智能和服务的兴趣与日俱增。在使用可扩展视频编码的多方 WebRTC 视频会议中,选择性转发单元(SFU)在带宽和终端异构的对等方之间提供连接。一个重要的问题是在网络的哪个位置放置 SFU 服务,以尽量减少所有对等点之间的端到端延迟。显然,对于所有可能的对等点位置,云 SFU 没有一个最佳位置。我们建议利用 NSP 边缘数据中心在网络边缘放置虚拟 SFU,以优化端到端延迟和整体网络资源的使用。分布式边缘-SFU 框架的主要优势在于,每个对等点的视频流通过最短路径到达其他对等点,类似于网状连接模型,而每个对等点向其边缘-SFU 上传单个视频流,避免了上传瓶颈。本文提出的分布式边缘-SFU 框架同时适用于尽力而为和托管服务模式,并通过 SDN 辅助的边缘网络切片,在接入网内提出了一种具有带宽和延迟保证的优质托管边缘集成多方 WebRTC 服务架构。实验结果证明了所提出的分布式边缘-SFU 服务架构的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed virtual selective-forwarding units and SDN-assisted edge computing for optimization of multi-party WebRTC videoconferencing
Network service providers (NSP) have growing interest in placing network intelligence and services at network edges by deploying software-defined network (SDN) and network function virtualization infrastructure. In multi-party WebRTC videoconferencing using scalable video coding, a selective forwarding unit (SFU) provides connectivity between peers with heterogeneous bandwidth and terminals. An important question is where in the network to place the SFU service in order to minimize end-to-end delay between all pairs of peers. Clearly, there is no single optimal place for a cloud SFU for all possible peer locations. We propose placing virtual SFUs at network edges leveraging NSP edge datacenters to optimize end-to-end delay and usage of overall network resources. The main advantage of the distributed edge-SFU framework is that each peer video stream travels the shortest path to reach other peers similar to mesh connection model, whereas each peer uploads a single stream to its edge-SFU avoiding the upload bottleneck. While the proposed distributed edge-SFU framework applies to both best-effort and managed service models, this paper proposes a premium managed, edge-integrated multi-party WebRTC service architecture with bandwidth and delay guarantees within access networks by SDN-assisted slicing of edge networks. The performance of the proposed distributed edge-SFU service architecture is demonstrated by means of experimental results.
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来源期刊
Signal Processing-Image Communication
Signal Processing-Image Communication 工程技术-工程:电子与电气
CiteScore
8.40
自引率
2.90%
发文量
138
审稿时长
5.2 months
期刊介绍: Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following: To present a forum for the advancement of theory and practice of image communication. To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems. To contribute to a rapid information exchange between the industrial and academic environments. The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world. Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments. Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.
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