Towards Highly Interactive Networked Multimedia Services with Crowd Intelligence

通过群体智能实现高度交互的网络多媒体服务

• 批准号: RGPIN-2019-04040
• 负责人: Liu, Jiangchuan
• 金额: $ 5.39万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714624
• 关键词: Towards; Highly; Interactive; Networked; Multimedia

With the advances in social, mobile and cloud computing, crowdsourcing has become the norm in today's digital media production and sharing, which utilizes the collective effort of users in the online community. With no doubt, networked services in the future will focus on the user experience and participation with rich media from the crowd. The boundless coverage of socialized media sharing however presents unprecedented scale challenges. Highly diversified content types, origins, and distribution channels further impose complex interactions among the individual participants, particularly for advanced immersive applications such as interactive livecast/gamecast, multi-party media production, online gaming and augmented reality (AR). Their demands on quality and responsiveness are much higher (e.g., latency in the order of 100 ms, or even 10 ms for AR) than conventional IPTV or VoIP. These rich interactions, on the other hand, provide rich and low cost data that were previously unavailable. Leveraging advanced data analytics and learning tools, the hidden intelligence from the crowd can be unveiled toward better system design. For instance, inferring game progress from live chat messages and tracking viewport through gaze patterns for resource reservation and display optimization. Yet uncalibrated data in the wild can be highly noisy, not to mention the intrinsic heterogeneity and dynamics of the Internet. Our research is guided by the following critical question: How can massive digital media content be effectively generated and delivered among the crowd with intensive online interactions and with the intelligence therein? Our long-term objective is to systematically examine the challenges in advanced interactive and crowdsourced multimedia services and to develop integrated solutions that will accommodate and utilize the interactions. A series of issues are to be addressed within the solution framework: (1) Online analysis of crowdsourced interaction data; (2) Advanced computing and communication architecture, in particular, cloud-edge collaboration for delay-sensitive applications; (3) Ultra-low-latency and -energy data transport. Beyond simply putting the individual results together, the design of individual modules will be revisited during system integration. This iterative process will eventually lead to an optimized coherent design, making the future multimedia services agile, efficient, and intelligent, and, in the long run, benefiting emerging applications without human-in-the-loop, such as assisted/autonomous driving. My team's broad expertise with state-of-the-art computing and communication systems has prepared us for exploring these new research frontiers. We will train highly qualified personnel and will nurture extensive collaboration with academic researchers worldwide, as well as with local and international industrial partners to pursue technology transfers, so as to raise Canada's global profile in this priority area.

随着社交、移动和云计算的进步，众包已经成为当今数字媒体生产和分享的常态，它利用了在线社区用户的集体努力。毫无疑问，未来的网络化服务将以用户体验和大众富媒体参与为重点。然而，社会化媒体分享的无限覆盖带来了前所未有的规模挑战。高度多样化的内容类型、来源和分销渠道进一步加剧了个体参与者之间的复杂互动，特别是对于交互式直播/游戏直播、多方媒体制作、在线游戏和增强现实（AR）等高级沉浸式应用程序。与传统的IPTV或VoIP相比，它们对质量和响应性的要求要高得多（例如，延迟在100毫秒左右，AR甚至是10毫秒）。