CAREER: Scarlet: Learned Protocols and Functional Architectures for Low-Latency Internet Video

职业：Scarlet：低延迟互联网视频的学习协议和功能架构

• 批准号: 2045714
• 负责人: Keith Winstein
• 金额: $ 68.62万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045714&HistoricalAwards=false
• 关键词: CAREER; Scarlet; Learned; Protocols; Functional

This project will create new approaches to the design of systems that transmit video and audio over the Internet in real time, improving on the commercial state-of-the-art (e.g., Zoom, Google Meet, Skype, or FaceTime). The hypothesis underlying this project is that new techniques in academic computer science (such as purely functional architectures, machine learning, and low-latency real-time distributed systems design) are ready to be applied successfully to this area. The ultimate goal will be to create new systems that allow people to feel like they are in the same place, able to perform music or theater or have a robust conversation while separated over the Internet.The project plans to pursue scientific inquiries in three areas: protocols, codecs, and compression. For protocols, the core idea is to apply Model-Predictive Control and in-situ learning to study an individual network path and predict its packet-by-packet dynamics at a fine grain, in order to control near-term behavior. For codecs, the main hypothesis is that incorporating machine learning tightly in an encoder's inner rate-control algorithm can reduce end-to-end latency and produce a proxy objective function that is useful to the transport protocol. If these efforts are successful, this opens up an opportunity for compression: the idea that low latency can be used to substitute for bandwidth, and that real-time gaze information and gaze-contingent compression can lead to manyfold compression gains and "retina-quality" 360x180 (virtual reality) video streaming over realistic path capacities.The project will include in a series of live online theatrical and musical performances, using far-flung actors and audience members connected over the Internet. The performances will serve as a learning opportunity and forcing function for the research project, an opportunity for performers who can't travel to be physically collocated, and a public demonstration of the benefits of computer-science research in an everyday setting. The project will include new classes taught for undergraduates at Stanford University on the development of video and audio transmission technology.The project will maintain a source-code repository at https://github.com/stanford-stagecast. Code will be released publicly as it is written; data and results will be published in the academic literature and will be available publicly (linked from the repository) as well. The project website will be maintained for at least three years.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.

该项目将创建新的方法，以实时通过Internet传输视频和音频的系统设计，从而改善商业最先进（例如Zoom，Zoom，Google Meet，Skype或FaceTime）。该项目的基础假设是，学术计算机科学中的新技术（例如纯粹的功能架构，机器学习和低延迟实时分布式系统设计）已准备好成功地应用于该领域。最终目标是创建新的系统，使人们感觉自己在同一地点，能够表演音乐或戏剧或在互联网上分开时进行稳健的对话。该项目计划在三个领域进行科学询问：协议，编解码器和压缩。对于协议，核心思想是应用模型预测性控制和原位学习来研究单个网络路径，并以细粒度预测其数据包动力学，以控制近期行为。对于编解码器而言，主要假设是将机器学习紧密地纳入编码器的内部速率控制算法可以减少端到端潜伏期，并产生对传输协议有用的代理目标函数。如果这些努力成功，这将为压缩打开机会：可以使用低潜伏期来代替带宽的想法，而实时凝视信息和凝视式压缩可以导致许多折叠的压缩增长和“视网膜质量”和“ 360x180（虚拟现实）（虚拟现实现实）的视频播放的播放器，则在线演出。成员通过互联网连接。这些表演将成为研究项目的学习机会和强迫功能，这是一个无法出差与身体上置的表演者的机会，并在日常环境中公开展示了计算机科学研究的好处。该项目将包括斯坦福大学（Stanford University）在本科生中教授的有关视频和音频传输技术开发的新课程。该项目将在https://github.com/stanford-stagecast上维护源代码存储库。代码将在书写时公开发布；数据和结果将发表在学术文献中，并且也将公开（从存储库链接）。该项目网站将至少维持三年。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛的影响审查标准的评估值得支持的。

项目成果

Sidecar: in-network performance enhancements in the age of paranoid transport protocols

Sidecar：偏执传输协议时代的网络内性能增强

• DOI: 10.1145/3563766.3564113
• 发表时间: 2022
• 期刊: The Twenty-first ACM Workshop on Hot Topics in Networks (HotNets 2022
• 作者: Yuan, Gina;Zhang, David K.;Sotoudeh, Matthew;Welzl, Michael;Winstein, Keith
• 通讯作者: Winstein, Keith

Computation-centric networking

以计算为中心的网络

• DOI: 10.1145/3563766.3564106
• 发表时间: 2022
• 期刊: HotNets
• 作者: Deng, Yuhan;Montemayor, Angela;Levy, Amit;Winstein, Keith
• 通讯作者: Winstein, Keith

R2E2: low-latency path tracing of terabyte-scale scenes using thousands of cloud CPUs

R2E2：使用数千个云CPU对TB级场景进行低延迟路径追踪

• DOI: 10.1145/3528223.3530171
• 发表时间: 2022
• 期刊: ACM Transactions on Graphics
• 影响因子: 6.2
• 作者: Fouladi, Sadjad;Shacklett, Brennan;Poms, Fait;Arora, Arjun;Ozdemir, Alex;Raghavan, Deepti;Hanrahan, Pat;Fatahalian, Kayvon;Winstein, Keith
• 通讯作者: Winstein, Keith