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
基本信息
- 批准号:1915122
- 负责人:
- 金额:$ 55.66万
- 依托单位:
- 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:
- 关键词:
项目摘要
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。 首先,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,毫米波是将融入5G无线网络的关键技术;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。该项目包括三个相互关联的研究重点。 (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点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
期刊论文数量(15)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:0
- 作者:Yanyuan Qin;Shuai Hao;K. Pattipati;Feng Qian;S. Sen;Bing Wang;Chaoqun Yue
- 通讯作者: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:
- 影响因子:0
- 作者:Arvind Narayanan;Eman Ramadan;Jason Carpenter;Qingxu Liu;Yu Liu;Feng Qian;Zhi-Li Zhang
- 通讯作者: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:
- 影响因子:0
- 作者:Qian, Feng;Han, Bo;Xiao, Qingyang;Gopalakrishnan, Vijay
- 通讯作者:Gopalakrishnan, Vijay
YuZu: Neural-Enhanced Volumetric Video Streaming
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Anlan Zhang;Chendong Wang;B. Han;Feng Qian
- 通讯作者: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:
- 影响因子:0
- 作者:Feng Qian;B. Han;Q. Xiao;V. Gopalakrishnan
- 通讯作者:Feng Qian;B. Han;Q. Xiao;V. Gopalakrishnan
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Feng Qian其他文献
A extreme learning machines approach for accurate estimation of large-scale IP network traffic matrix
一种精确估计大规模IP网络流量矩阵的极限学习机方法
- DOI:
- 发表时间:
2012 - 期刊:
- 影响因子:0
- 作者:
Feng Qian;Tao Lian;Jiaxing Wu - 通讯作者:
Jiaxing Wu
Investigating the coupling between phytoplankton biomass, aerosol optical depth and sea-ice cover in the Greenland Sea
研究格陵兰海浮游植物生物量、气溶胶光学深度和海冰覆盖之间的耦合
- DOI:
10.1016/j.dynatmoce.2014.03.001 - 发表时间:
2014-06 - 期刊:
- 影响因子:1.7
- 作者:
Hailang Lu;Dao Rong Lin;Feng Qian;Min Zhao - 通讯作者:
Min Zhao
Unsupervised Estimation of Monocular Depth and VO in Dynamic Environments via Hybrid Masks
通过混合掩模对动态环境中的单眼深度和 VO 进行无监督估计
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:10.4
- 作者:
Qiyu Sun;Yang Tang;Chongzhen Zhang;Chaoqiang Zhao;Feng Qian;Jürgen Kurths - 通讯作者:
Jürgen Kurths
Oscillatory behavior of a class impulsive fractional partial differential equation
一类脉冲分数阶偏微分方程的振荡行为
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Feng Qian;Liu Anping - 通讯作者:
Liu Anping
Reversible Swarming of Micro Robots Controlled by Acoustic Field
声场控制微型机器人的可逆集群
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Cong Zhao;Hu Ou;Lukai Shi;Ying Wei;Feng Qian;Xiaolong Lu - 通讯作者:
Xiaolong Lu
Feng Qian的其他文献
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{{ truncateString('Feng Qian', 18)}}的其他基金
Conference: ACM SIGCOMM 2023 Travel Grant
会议:ACM SIGCOMM 2023 旅行补助金
- 批准号:
2335184 - 财政年份:2023
- 资助金额:
$ 55.66万 - 项目类别:
Standard Grant
Collaborative Research: CNS Core: Medium: Innovating Volumetric Video Streaming with Motion Forecasting, Intelligent Upsampling, and QoE Modeling
合作研究:CNS 核心:中:通过运动预测、智能上采样和 QoE 建模创新体积视频流
- 批准号:
2409008 - 财政年份:2023
- 资助金额:
$ 55.66万 - 项目类别:
Continuing Grant
Collaborative Research: SaTC: CORE: Medium: Audacity of Exploration: Toward Automated Discovery of Security Flaws in Networked Systems through Intelligent Documentation Analysis
协作研究:SaTC:核心:中:大胆探索:通过智能文档分析自动发现网络系统中的安全缺陷
- 批准号:
2409269 - 财政年份:2023
- 资助金额:
$ 55.66万 - 项目类别:
Standard Grant
CPS: Medium: Collaborative Research: Transforming Connected and Automated Transportation with Smart Networking, Cooperative Sensing, and Edge Computing
CPS:中:协作研究:通过智能网络、协作传感和边缘计算改变互联和自动化交通
- 批准号:
2409271 - 财政年份:2023
- 资助金额:
$ 55.66万 - 项目类别:
Standard Grant
Collaborative Research: CNS Core: Medium: Innovating Volumetric Video Streaming with Motion Forecasting, Intelligent Upsampling, and QoE Modeling
合作研究:CNS 核心:中:通过运动预测、智能上采样和 QoE 建模创新体积视频流
- 批准号:
2212298 - 财政年份:2022
- 资助金额:
$ 55.66万 - 项目类别:
Continuing Grant
Collaborative Research: SaTC: CORE: Medium: Audacity of Exploration: Toward Automated Discovery of Security Flaws in Networked Systems through Intelligent Documentation Analysis
协作研究:SaTC:核心:中:大胆探索:通过智能文档分析自动发现网络系统中的安全缺陷
- 批准号:
2154078 - 财政年份:2022
- 资助金额:
$ 55.66万 - 项目类别:
Standard Grant
Collaborative Research: CNS Core: Medium: Foundations and Scalable Algorithms for Personalized and Collaborative Virtual Reality Over Wireless Networks
协作研究:CNS 核心:中:无线网络上个性化和协作虚拟现实的基础和可扩展算法
- 批准号:
2106090 - 财政年份:2021
- 资助金额:
$ 55.66万 - 项目类别:
Continuing Grant
CPS: Medium: Collaborative Research: Transforming Connected and Automated Transportation with Smart Networking, Cooperative Sensing, and Edge Computing
CPS:中:协作研究:通过智能网络、协作传感和边缘计算改变互联和自动化交通
- 批准号:
2038559 - 财政年份:2021
- 资助金额:
$ 55.66万 - 项目类别:
Standard Grant
XPS: FULL: Collaborative Research: Enabling Scalable Cloud And Edge-device Integration Using Cross-layer Parallelism
XPS:完整:协作研究:使用跨层并行性实现可扩展的云和边缘设备集成
- 批准号:
1903880 - 财政年份:2018
- 资助金额:
$ 55.66万 - 项目类别:
Standard Grant
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:
1750890 - 财政年份:2018
- 资助金额:
$ 55.66万 - 项目类别:
Continuing Grant
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