CNS Core: Small: Collaborative: Content-Based Viewport Prediction Framework for Live Virtual Reality Streaming

CNS 核心：小型：协作：用于直播虚拟现实流的基于内容的视口预测框架

• 批准号: 1910085
• 负责人: Sheng Wei
• 金额: $ 21.49万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910085&HistoricalAwards=false
• 关键词: CNS; Core; Small; Collaborative; Content

Virtual reality (VR) video streaming has been gaining popularity recently with the rapid adoption of mobile head mounted display (HMD) devices in the consumer video market. As the cost for the immersive experience drops, VR video streaming introduces new bandwidth and performance challenges, especially in live streaming, due to the delivery of 360-degree views. This project develops a new content-based viewport prediction framework to improve the bandwidth and performance in live VR streaming, which predicts the user's viewport through a fusion of tracking the moving objects in the video, extracting the video semantics, and modeling the user's viewport of interest.This project consists of three research thrusts. First, it develops a content-based viewport prediction framework for live VR streaming by tracking the motions and semantics of the objects. Second, it employs hardware and software techniques to facilitate real-time execution and scale the viewport prediction mechanism to a large number of users. Third, it develops evaluation frameworks to verify the functionality, performance, and scalability of the approach. The project uniquely considers the correlation between video content and user behavior, which leverages the deterministic nature of the former to conquer the randomness of the latter.With the rapidly increasing popularity of VR systems in domain-specific immersive environments, the project will benefit several VR-related fields of studies with significant bandwidth savings and performance improvements, such as VR-based live broadcast, healthcare, and scientific visualization. Moreover, the interdisciplinary nature of the project will enhance the education and recruitment of underrepresented minorities in several science, technology, engineering, and mathematics (STEM) fields.The project repository will be stored on a publicly accessible server (https://github.com/hwsel). All the project data will be maintained for at least five years following the end of the grant period.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.

随着移动头戴式显示器（HMD）设备在消费视频市场的迅速采用，虚拟现实（VR）视频流最近越来越受欢迎。随着沉浸式体验成本的下降，VR视频流带来了新的带宽和性能挑战，特别是在直播中，因为要提供360度的视角。该项目开发了一个新的基于内容的视口预测框架，以提高实时VR流的带宽和性能，该框架通过跟踪视频中移动物体的融合来预测用户的视口，提取视频语义，并对用户感兴趣的视口建模。这个项目包括三个研究重点。首先，它通过跟踪对象的运动和语义，为实时VR流开发了一个基于内容的视口预测框架。其次，采用硬件和软件技术促进实时执行，并将视口预测机制扩展到大量用户。第三，开发评估框架来验证方法的功能、性能和可伸缩性。该项目独特地考虑了视频内容与用户行为之间的相关性，利用前者的确定性来克服后者的随机性。随着VR系统在特定领域沉浸式环境中的迅速普及，该项目将有利于几个VR相关领域的研究，显著节省带宽和提高性能，如基于VR的直播、医疗保健和科学可视化。此外，该项目的跨学科性质将加强在若干科学、技术、工程和数学（STEM）领域中代表性不足的少数民族的教育和招聘。项目存储库将存储在一个可公开访问的服务器（https://github.com/hwsel）上。所有项目数据将在资助期结束后至少保存五年。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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

LiveROI: region of interest analysis for viewport prediction in live mobile virtual reality streaming

• DOI: 10.1145/3458305.3463378
• 发表时间: 2021-07
• 期刊: Proceedings of the 12th ACM Multimedia Systems Conference
• 作者: Xianglong Feng;Weitian Li;Sheng Wei

Power-efficient live virtual reality streaming using edge offloading

• DOI: 10.1145/3534088.3534351
• 发表时间: 2022-06
• 期刊: Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video
• 作者: Ziehen Zhu;Xianglong Feng;Zhongze Tang;Nan Jiang;Tian Guo;Lisong Xu;Sheng Wei

LiveObj: Object Semantics-based Viewport Prediction for Live Mobile Virtual Reality Streaming

• DOI: 10.1109/tvcg.2021.3067686
• 发表时间: 2021-04
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Xianglong Feng;Zeyang Bao;Sheng Wei

LiveDeep: Online Viewport Prediction for Live Virtual Reality Streaming Using Lifelong Deep Learning

LiveDeep：使用终身深度学习进行实时虚拟现实流的在线视口预测

• DOI: 10.1109/vr46266.2020.00104
• 发表时间: 2020
• 期刊: IEEE Conference on Virtual Reality and 3D User Interfaces (VR
• 作者: Feng, Xianglong;Liu, Yao;Wei, Sheng
• 通讯作者: Wei, Sheng