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.

在移动设备上无线流媒体视频是一种日益重要的应用。该项目的目标是为新内容类型和新兴网络的移动视频交付带来创新。具体地说，该项目研究了三个方面：(1)360度身临其境的视频传输，(2)多网络路径上的视频流(多路径)，以及(3)毫米波(毫米波)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块。首先，360度视频为用户提供了独特的全景观看体验；然而，360度视频内容交付比常规视频更具挑战性。其次，多个网络接口已成为现成移动设备的标准，但其潜力远未得到充分开发。第三，MmWave是一项关键技术，将被整合到5G无线网络中；但将视频流适应MmWave在很大程度上仍是一个未知领域。建议的解决方案将通过提升用户体验和降低下一代沉浸式视频服务的资源消耗来造福社会。这项研究还将与一项教育计划相结合，该计划旨在帮助计算机科学专业的学生了解网络和系统方面的新技术趋势，并激发公众对科学、技术、工程和数学的普遍兴趣。该项目包括三个相互关联的研究推动力。(1)对于360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引导流的关键前提。它还使用了一种速率自适应方案，该方案采用了“增量编码”的设计，允许以增量方式升级获取的块的质量。这大大提高了在面对头部运动的随机性时的适应性。(2)对于多路径流媒体，本项目使用多个网络接口同时用于视频流媒体。该网络框架支持视频速率自适应，允许用户灵活配置每条路径的成本。该框架还通过策略分组调度来支持多路径上的延迟敏感型直播。(3)毫米波链路具有容量大、间歇可用的独特特点。该项目首先为毫米波链路设计了传输层。然后，它提出了几种针对MmWave量身定做的视频流策略，例如战略性地将MmWave和传统的全向无线电相结合。对于上述研究，该项目将开发算法、模型和系统，并得到实际实施和评估的支持。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