Low-Latency Streaming and Storage Systems using Error Correcting Codes

未来的互联网有望支持各种低延迟的沉浸式多媒体应用，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不能满足这些应用中严格的延迟目标。拟议的研究将开发新的纠错码（ECC），用于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善。虽然ECC在通信和存储系统中具有丰富的历史，但它们在低延迟流应用中的实用性在学术研究或真实的世界系统中都没有引起太大的兴趣。相反，传统的ECC设计涉及非常长的块长度，这会引入显著的延迟。这种ECC在低延迟应用中的幼稚应用将极大地损害其纠错能力，或显著增加延迟。我们认为，这样的ECC不应该被用于流媒体应用。然而，如果一个明智地开发新的编码方案，从第一原则，然后可以实现显着的改进。为此，我们将研究ECC的基本限制，在流媒体应用中使用的技术，从信息论。虽然这样的研究几十年来一直是一个悬而未决的问题，但最近PI和他的合作者通过开发一种新的攻击路线来解决这个问题，取得了重大突破。使用这种方法，所提出的研究准备在流媒体应用的低延迟ECC领域做出一些开创性的贡献。此外，这项研究将是利用机器学习方法设计ECC以不断适应不断变化的网络条件的极少数努力之一。 当人们考虑按需流应用时，通过在网络边缘缓存流行内容来减少延迟的主要机会。新兴的无线网络将由基站和接入点的超密集网络组成，并为它们提供本地存储数据的能力。通过在这些系统中应用ECC，我们可以将单个设备上的存储转换为单个虚拟缓存，从而显著提高存储效率和可靠性。拟议的研究将利用ECC在存储系统中的最新进展，包括分布式存储代码和编码缓存，从第一原则出发在存储系统中开发延迟优化的ECC，并将其与提案第一部分中讨论的流通信系统的ECC集成。 拟议的研究有可能大大降低直播和点播流媒体应用程序的成本，通过以较低的成本支持高端网真通信来减少商务旅行，并为加拿大提供环境和经济效益。

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.