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，直播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播放社交直播视频流现在无处不在。这种通信经常面临中断，因为从发送方传输的数据可能在通信介质中丢失或延迟，无法及时到达接收方。为了避免由于这种丢失而产生的毛刺，发送方侧的应用程序需要以冗余方式发送数据，以便即使在原始流的某些部分丢失时也可以恢复原始流。增加这种冗余会消耗额外的带宽，这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量有一个基本的了解，并将设计执行这种损失恢复直播流通信的实用和有效的方法。该项目的研究结果将纳入研究生和本科生课程。将开展定向努力，促进STEM教育和指导本科生的多样性。实时流传输通信涉及以严格的延迟约束顺序地编码和解码分组。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。这个项目的首要目标是建立一个通用模型下的实时，延迟受限的流通信编码的基础。具体而言，该项目在两个大方向上启动研究：（1）处理可变到达大小：流编码的现有模型考虑“固定大小”设置，其中所有到达的源数据包具有相同的大小，这一假设通常在实践中不成立，从而降低性能。该项目提出了一个广义模型，将到达大小的变化，并调查了新的问题空间，包括一个新的权衡所产生的变化，并构造最佳的流代码的新模型。(2)为相关随机信道设计流代码：网络上数据包擦除的常见来源本质上是突发的，从而导致相关丢失。现有的关于实时、延迟受限的流媒体通信的工作主要研究了仅在对抗性设置下的相关损失，这导致了显著的带宽开销。该项目研究为包含相关损耗的随机信道模型构建流代码。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.