CAREER: Improving Mobile Video Delivery for Emerging Contents and Networks

职业：改进新兴内容和网络的移动视频传输

• 批准号: 1915122
• 负责人: Feng Qian
• 金额: $ 55.66万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-27 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915122&HistoricalAwards=false
• 关键词: CAREER; Improving; Mobile; Video; Delivery

Streaming videos wirelessly on mobile devices is an increasingly important application. The objective of this project is to bring innovations to mobile video delivery for new content types and over emerging networks. Specifically, the project investigates three aspects: (1) 360-degree immersive video delivery, (2) video streaming over multiple network paths (multipath), and (3) video streaming over millimeter-wave (mmWave) links. These are expected to be the key building blocks of next-generation video streaming services. First, 360-degree videos provide users with unique panoramic viewing experience; however, 360-degree video content delivery is much more challenging compared to regular videos. Second, multiple network interfaces have become a norm on off-the-shelf mobile devices but their potential is far from being fully exploited. Third, mmWave is a key technology that will be integrated into 5G wireless networks; but adapting video streaming to mmWave largely remains an uncharted territory. The proposed solutions will benefit the society by enhancing the user experience and reducing the resource consumption for next-generation immersive video services. The research will also be integrated with an education plan that seeks to prepare computer science students with the knowledge of new technological trends in networking and systems, and stimulate the general public interest in Science, Technology, Engineering, and Mathematics.This project includes three inter-connected research thrusts. (1) For 360 video streaming, based on the concept of field-of-view (FoV) guided streaming, the project uses big data analytics to facilitate accurate head movement prediction, a key prerequisite for FoV-guided streaming. It also uses a rate adaptation scheme with a "delta encoding" design allowing the quality of a fetched chunk to be incrementally upgraded. This substantially improves adaptability when facing randomness in head movements. (2) For multipath streaming, the project uses multiple network interfaces to be used simultaneously for streaming videos. The network framework supports video rate adaptation and allows users to flexibly configure each path's cost. The framework also supports delay-sensitive live streaming over multipath through strategic packet scheduling. (3) mmWave links bear unique characteristics of massive capacity and intermittent availability. The project first designs a transport layer for mmWave links. It then proposes several video streaming strategies tailored to mmWave, such as strategically combining mmWave and legacy omni-directional radios. For the above research thrusts, the project will develop algorithms, models, and systems, backed up by real implementation and evaluation.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.

在移动设备上无线传输视频是一个日益重要的应用。该项目的目标是为新内容类型和新兴网络的移动视频传输带来创新。具体来说，该项目研究三个方面：(1) 360 度沉浸式视频传输，(2) 多个网络路径（多路径）上的视频流，以及 (3) 毫米波 (mmWave) 链路上的视频流。这些预计将成为下一代视频流服务的关键构建模块。 第一，360度视频为用户提供独特的全景观看体验；然而，与普通视频相比，360 度视频内容交付更具挑战性。其次，多个网络接口已成为现成移动设备的常态，但其潜力还远未得到充分利用。第三，毫米波是将集成到5G无线网络中的关键技术；但使视频流适应毫米波在很大程度上仍然是一个未知领域。所提出的解决方案将通过增强用户体验并减少下一代沉浸式视频服务的资源消耗来造福社会。该研究还将与一项教育计划相结合，旨在让计算机科学专业的学生了解网络和系统的新技术趋势，并激发公众对科学、技术、工程和数学的兴趣。该项目包括三个研究项目-相关的研究重点。 (1) 对于 360 度视频流，基于视场 (FoV) 引导流的概念，该项目使用大数据分析来促进准确的头部运动预测，这是 FoV 引导流的关键先决条件。它还使用具有“增量编码”设计的速率自适应方案，允许逐步升级所获取的块的质量。这大大提高了面对头部运动随机性时的适应性。 (2) 对于多路径流媒体，该项目使用多个网络接口同时用于流媒体视频。网络框架支持视频速率适配，并允许用户灵活配置每条路径的成本。该框架还通过策略数据包调度支持多路径上的延迟敏感实时流媒体。 (3)毫米波链路具有海量容量和间歇可用性的独特特性。该项目首先设计毫米波链路的传输层。然后，它提出了几种针对毫米波定制的视频流策略，例如战略性地结合毫米波和传统全向无线电。 对于上述研究主旨，该项目将开发算法、模型和系统，并以实际实施和评估为支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势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

Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

• DOI: 10.1145/3304109.3306231
• 发表时间: 2019-06
• 期刊: Proceedings of the 10th ACM Multimedia Systems Conference
• 作者: Yanyuan Qin;Shuai Hao;K. Pattipati;Feng Qian;S. Sen;Bing Wang;Chaoqun Yue

A First Look at Commercial 5G Performance on Smartphones

• DOI: 10.1145/3366423.3380169
• 发表时间: 2019-09
• 期刊: Proceedings of The Web Conference 2020
• 作者: Arvind Narayanan;Eman Ramadan;Jason Carpenter;Qingxu Liu;Yu Liu;Feng Qian;Zhi-Li Zhang

Demo: Tile-Based Viewport-Adaptive Panoramic Video Streaming on Smartphones

演示：智能手机上基于图块的视口自适应全景视频流

• DOI: 10.1145/3241539.3267715
• 发表时间: 2018
• 期刊: The 24th Annual International Conference on Mobile Computing and Networking
• 作者: Qian, Feng;Han, Bo;Xiao, Qingyang;Gopalakrishnan, Vijay
• 通讯作者: Gopalakrishnan, Vijay

YuZu: Neural-Enhanced Volumetric Video Streaming

• 发表时间: 2022
• 作者: Anlan Zhang;Chendong Wang;B. Han;Feng Qian

Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devices

• DOI: 10.1145/3241539.3241565
• 发表时间: 2018-10
• 期刊: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking
• 作者: Feng Qian;B. Han;Q. Xiao;V. Gopalakrishnan