ICE-T:RC: Optimizing Internet video through support from the network edge

ICE-T:RC：通过网络边缘的支持优化互联网视频

• 批准号: 1836889
• 负责人: Sanjay Rao
• 金额: $ 30万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836889&HistoricalAwards=false
• 关键词: ICE; RC; Optimizing; Internet; video

Despite many advances in recent years, the performance of Internet video often falls below user expectations. The issues are likely to be exacerbated with the emergence of bandwidth-intensive 4K video, and more recently, interactive video such as 360-degree video which is latency sensitive as well. This project investigates new opportunities opened by recent Internet Service Provider (ISP) interest in deploying value-added services at the edge of the provider's network. Such edge-based services can aid video streaming applications through lower latencies, higher throughput, and visibility into network performancebottlenecks.This project will explore novel services that may be deployed at the ISP edge, which can significantly improve the performance of video streaming solutions, as well as develop new approaches to video streaming that can leverage these services. The research will systematically characterize these benefits, develop new mechanisms, and consider practical deployment concerns. Specific research contributions will include: 1. Techniques for proactively estimating the rate at which video applications can transmit informed by explicit information collected at the network edge in real-time. The approach includes edge-based monitoring and estimation to ensure scalability, and extensive use of network data to refine and validate our algorithms.2. Redesigning state-of-the-art available bit rate (ABR) approaches to exploit proactive rate estimation. The project team will also explore a new design point where the ABR algorithm itself is run on an edge server rather than the client.3. Measurement-based characterization studies of interactive video streaming to investigate issues around edge support for interactive video. Specifically, the project will consider the interplay between the encoding scheme used for 360-degree video, user interactivity models, and the edge cache hit rate. This project will extend an ongoing research collaboration between Purdue University and the Politecnico di Torino, Italy.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.

尽管近年来取得了许多进步，但互联网视频的性能常常低于用户的期望。随着带宽密集型 4K 视频以及最近对延迟敏感的 360 度视频等交互式视频的出现，这些问题可能会加剧。 该项目调查了最近互联网服务提供商 (ISP) 对在提供商网络边缘部署增值服务的兴趣所带来的新机遇。这种基于边缘的服务可以通过更低的延迟、更高的吞吐量和对网络性能瓶颈的可见性来帮助视频流应用程序。该项目将探索可以部署在 ISP 边缘的新颖服务，这可以显着提高视频流解决方案的性能，例如以及开发可以利用这些服务的新视频流方法。该研究将系统地描述这些好处，开发新机制，并考虑实际部署问题。具体的研究贡献将包括： 1. 主动估计视频应用程序传输在网络边缘实时收集的显式信息的速率的技术。该方法包括基于边缘的监控和估计以确保可扩展性，以及广泛使用网络数据来完善和验证我们的算法。2。重新设计最先进的可用比特率 (ABR) 方法以利用主动速率估计。项目团队还将探索一个新的设计点，即ABR算法本身在边缘服务器而不是客户端上运行。3．基于测量的交互式视频流特性研究，以调查交互式视频边缘支持的问题。具体来说，该项目将考虑 360 度视频编码方案、用户交互模型和边缘缓存命中率之间的相互作用。 该项目将扩大普渡大学和意大利都灵理工大学之间正在进行的研究合作。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Exploring the interplay between CDN caching and video streaming performance

• DOI: 10.1109/infocom41043.2020.9155338
• 发表时间: 2020-07
• 期刊: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
• 作者: Ehab Ghabashneh;Sanjay G. Rao

Understanding Video Management Planes

了解视频管理平面

• DOI: 10.1145/3278532.3278554
• 发表时间: 2018
• 期刊: Internet Measurement Conference
• 作者: Akhtar, Zahaib;Nam, Yun Seong;Chen, Jessica;Govindan, Ramesh;Katz-Bassett, Ethan;Rao, Sanjay;Zhan, Jibin;Zhang, Hui
• 通讯作者: Zhang, Hui

Chimera: exploiting UAS flight path information to optimize heterogeneous data transmission

• DOI: 10.1109/icnp52444.2021.9651923
• 发表时间: 2021-11
• 期刊: 2021 IEEE 29th International Conference on Network Protocols (ICNP)
• 作者: Russell Shirey;Sanjay G. Rao;S. Sundaram

Xatu: Richer Neural Network Based Prediction for Video Streaming

• DOI: 10.1145/3489048.3522641
• 发表时间: 2022-06
• 期刊: Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
• 作者: Yun Seong Nam;Jianfei Gao;Chandan Bothra;Ehab Ghabashneh;Sanjay G. Rao;Bruno Ribeiro;Jibin Zhan;Hui Zhang

Pitfalls of data-driven networking: A case study of latent causal confounders in video streaming

• DOI: 10.1145/3405671.3405815
• 发表时间: 2020-08
• 期刊: Proceedings of the Workshop on Network Meets AI & ML
• 作者: P. C. Sruthi;Sanjay G. Rao;Bruno Ribeiro