NeTS: Small: Flexible Delivery of Streaming Video using Network-Aware MD-FEC Coding

NeTS：小型：使用网络感知 MD-FEC 编码灵活传输流视频

• 批准号: 1018398
• 负责人: Koushik Kar
• 金额: $ 40万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018398&HistoricalAwards=false
• 关键词: NeTS; Small; Flexible; Delivery; Streaming

Streaming video is envisioned to become the ?killer? networking application in the coming decade, and may determine whether the Internet and the current wireless technologies will live up to the explosive growth of multimedia demands of the next-generation network users. This project aims to develop coding and distribution mechanisms for transferring video over networks in a scalable and effective manner, which will be critical in meeting this challenge. In particular, the project investigates the design of a broad theoretical framework as well as specific mechanisms that would enable efficient delivery of streaming video through the use of video distribution networks (VDNs) and ?network-aware? Multiple Description Forward Error Correction (MD-FEC) coding. MD-FEC can be very effective in minimizing the overall distortion in video quality when delivering streaming video in a typical network environment that experiences time-varying bandwidth fluctuations and random packet losses. The project also explores how network coding can be integrated with MD-FEC coding to enhance live video streaming performance. This cross-disciplinary research is being done through a closely-integrated effort by an interdisciplinary team bringing together expertise in the areas of video coding and information theory; and network modeling, optimization and simulation. The results of this research project are expected to provide key insights on how streaming live video should be delivered for optimal performance over the Internet and last-hop wireless networks in the future. Proactive efforts are also being taken towards integrating the research findings into related graduate courses, and involving undergraduates in these research activities.

流媒体视频被设想为“杀手”。网络应用的发展趋势，并可能决定互联网和目前的无线技术能否满足下一代网络用户爆炸性增长的多媒体需求。该项目旨在开发编码和分发机制，以可扩展和有效的方式在网络上传输视频，这将是应对这一挑战的关键。特别是，该项目研究了一个广泛的理论框架的设计，以及通过使用视频分发网络（vdn）和“网络感知”实现流媒体视频有效传输的具体机制。多重描述前向纠错（MD-FEC）编码。当在经历时变带宽波动和随机数据包丢失的典型网络环境中传输流视频时，MD-FEC可以非常有效地将视频质量的整体失真降至最低。该项目还探讨了如何将网络编码与MD-FEC编码集成，以提高实时视频流的性能。这一跨学科研究是通过一个跨学科小组的紧密整合努力进行的，该小组汇集了视频编码和信息理论领域的专门知识；并对网络进行建模、优化和仿真。该研究项目的结果有望为未来如何通过互联网和最后一跳无线网络提供流媒体直播视频的最佳性能提供关键见解。目前还在积极努力将研究成果纳入有关的研究生课程，并使本科生参与这些研究活动。