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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737358
• 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，众包已成为当今数字媒体制作和共享的常态，它利用了在线社区中用户的集体努力。毫无疑问，未来的网络服务将侧重于用户体验和人群富媒体参与。然而，社交媒体共享的无边覆盖带来了前所未有的规模挑战。高度多样化的内容类型、来源和分发渠道进一步加剧了个体参与者之间复杂的交互，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。他们对质量和响应能力的要求比传统 IPTV 或 VoIP 高得多（例如，延迟约为 100 毫秒，对于 AR 甚至为 10 毫秒）。 另一方面，这些丰富的交互提供了以前无法获得的丰富且低成本的数据。利用先进的数据分析和学习工具，可以揭示人群中隐藏的情报，从而实现更好的系统设计。例如，从实时聊天消息推断游戏进度，并通过注视模式跟踪视口以进行资源预留和显示优化。然而，未经校准的野外数据可能非常嘈杂，更不用说互联网固有的异质性和动态性了。我们的研究以以下关键问题为指导：如何通过密集的在线互动和其中的智能在人群中有效生成和传递海量数字媒体内容？我们的长期目标是系统地研究先进的交互式和众包多媒体服务中的挑战，并开发适应和利用交互的集成解决方案。该解决方案框架需要解决一系列问题：（1）众包交互数据的在线分析； （2）先进的计算和通信架构，特别是时延敏感应用的云边协同； (3)超低时延、低能耗的数据传输。除了简单地将各个结果放在一起之外，系统集成期间还将重新审视各个模块的设计。这个迭代过程最终将带来优化的连贯设计，使未来的多媒体服务变得敏捷、高效和智能，从长远来看，将有利于无需人在环的新兴应用，例如辅助/自动驾驶。我的团队在最先进的计算和通信系统方面拥有广泛的专业知识，为我们探索这些新的研究前沿做好了准备。我们将培养高素质人才，并与世界各地的学术研究人员以及本地和国际工业合作伙伴开展广泛的合作，以寻求技术转让，从而提高加拿大在这一优先领域的全球形象。