Low-Latency Streaming and Storage Systems using Error Correcting Codes

使用纠错码的低延迟流和存储系统

• 批准号: RGPIN-2019-05797
• 负责人: Khisti, Ashish
• 金额: $ 4.01万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715669
• 关键词: Low; Latency; Streaming; Storage; Systems

The future Internet is expected to support a variety of low-latency immersive multimedia applications including high-end telepresence, augmented/virtual reality and online gaming. Unfortunately state-of-the-art communication and storage systems still fall short of meeting the stringent latency targets in these applications. The proposed research will develop new error correcting codes (ECC) for supporting communication and storage systems that can achieve orders of magnitude improvement in latency for multimedia applications. Although ECC have a rich history in both communication and storage systems, their utility in low-latency streaming applications has not received much interest in either academic research or real world systems. On the contrary, traditional ECC designs involve very long block-lengths which can introduce significant delays. A naive application of such ECC in low-latency applications will drastically compromise their error correction capability, or significantly increase latency. We argue that such ECC should not be used in streaming applications. However if one judiciously develops new coding schemes, from first principles, then significant improvements can be achieved. Towards this end, we will study fundamental limits of ECC in streaming applications using techniques from information theory. Although such a study has remained an open problem for several decades, recently the PI and his collaborators have achieved major breakthroughs by developing a new line of attack on this problem. Using this methodology, the proposed research is poised to make several pioneering contributions in the area of low-latency ECC for streaming applications. In addition, the proposed research will be one of the very few efforts to make use of machine learning methods in designing ECC that continuously adapt to changing network conditions. When one considers on-demand streaming applications, a major opportunity for reducing latency exists by caching popular content at the network edge. Emerging wireless networks will consist of an ultra dense network of base stations and access points and provide them with the ability to store data locally. By applying ECC in these systems, we can turn the storage at individual devices into a single virtual cache with significantly better storage efficiency and reliability. The proposed research will leverage on recent advancements in ECC in storage systems including distributed storage codes and coded caching, develop latency-optimized ECC in storage systems from first principles, and integrate them with ECC for streaming communication systems discussed in the first part of the proposal. The proposed research has the potential to dramatically reduce cost of both live streaming and on-demand streaming applications, reduce business travel by supporting high-end telepresence communication at a lower cost, and provide both environmental and economic benefits to Canada.

未来的互联网有望支持各种低延迟沉浸式多媒体应用，包括高端远程呈现、增强/虚拟现实和在线游戏。不幸的是，最先进的通信和存储系统仍然无法满足这些应用中严格的延迟目标。提出的研究将开发新的纠错码（ECC），以支持通信和存储系统，可以实现多媒体应用延迟的数量级改进。