CIF: Small: Coding for Live Delay-constrained Streaming Communication

由于人们交流、分享和消费内容的方式发生了范式转变，实时流媒体通信正在迅速成为我们社会不可或缺的一部分。视频会议、通过电视频道的流媒体观看高清体育赛事以及在商业平台上广播社交直播视频流现在已经无处不在。这种通信经常面临中断，因为从发送方传输的数据可能会在通信介质中丢失或延迟，并且无法及时到达接收方。为了避免由于此类丢失而导致的故障，发送方的应用程序需要以冗余方式发送数据，以便即使原始流的某些部分丢失也可以恢复。添加这种冗余会消耗额外的带宽，而带宽是一种稀缺且昂贵的资源。该项目将对传输具有所需质量的直播流所需的最小额外带宽有一个基本的了解，并且还将设计执行此类抗丢失直播流通信的实用且有效的方法。该项目的研究结果将纳入研究生和本科生课程。将开展有针对性的努力，促进 STEM 教育的多样性和指导本科生。实时流通信涉及具有严格延迟约束的顺序编码和解码数据包。由于严格的延迟约束，使用重传的丢失恢复通常不适用，并且需要前向纠错。该项目的总体目标是在通用模型下建立实时、延迟受限的流通信编码的基础。具体来说，该项目在两个大方向上启动了研究：（1）处理可变的到达大小：现有的流代码模型考虑“固定大小”设置，其中所有到达的源数据包具有相同的大小，但这种假设在实践中通常不成立，从而降低了性能。该项目提出了一种包含到达大小可变性的广义模型，并研究了新的问题空间，包括由可变性产生的新权衡，并为新模型构建了最佳流代码。 (2) 为相关随机信道设计流编码：网络上数据包擦除的常见来源本质上是突发性的，从而导致相关丢失。现有的实时、延迟受限的流通信工作主要研究了仅在对抗性设置下的相关损失，这导致了巨大的带宽开销。该项目研究构建包含相关损失的随机通道模型的流代码。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

Live streaming communication is fast becoming an integral part of our society due to a paradigm shift in the way people communicate, and share and consume content. Video conferencing, watching high-definition sporting events over streaming counterparts to television channels, and broadcasting social live video streams on commercial platforms are now ubiquitous. Such communications often face disruptions since data transmitted from the sender can get lost or delayed in the communication medium and not reach the receiver in time. In order to avoid the resulting glitches due to such losses, the application on the sender side needs to send data in a redundant fashion such that the original stream can be recovered even when some parts of it are lost. Adding such redundancy consumes additional bandwidth which is a scarce and costly resource. This project will develop a fundamental understanding of the minimum amount of additional bandwidth needed to transmit a live stream with a desired quality, and also will design practical and efficient ways of performing such loss-resilient live streaming communication. The findings from this project will be integrated into graduate and undergraduate courses. Directed efforts will be undertaken for promoting diversity in STEM education and in mentoring undergraduate students. Live streaming communication involves sequentially encoding and decoding packets with a strict delay constraint. Due to the strict delay constraint, loss recovery using retransmissions are often not applicable and forward error correction is needed. The overarching goal of this project is to establish the fundamentals of coding for live, delay-constrained streaming communication under a generalized model. Specifically, the project initiates studies in two broad directions: (1) Handling variable arrival-sizes: Existing models on streaming codes consider a "fixed-size" setup, where all arriving source packets have identical sizes, an assumption which often does not hold in practice, thereby degrading performance. This project proposes a generalized model incorporating variability in arrival sizes and investigates the new problem space, including a new tradeoff resulting from the variability, and constructs optimal streaming codes for the new model. (2) Designing streaming codes for correlated stochastic channels: Common sources of packet erasures over the network are bursty in nature, thereby resulting in correlated losses. The existing body of work on live, delay-constrained streaming communication has largely studied correlated losses only under adversarial settings, which result in significant bandwidth overhead. This project investigates constructing streaming codes for stochastic channel models incorporating correlated losses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.