Collaborative Research: Redesigning Video Streaming Infrastructure to Systematically Reduce Mobile Device Video Display Energy Consumption in the Video-rich Era

摘要：本研究的目的是开发新的解决方案，通过重新设计整个视频流基础设施来系统地降低未来富视频移动设备的能耗。显示器能耗占移动设备能耗的比例很高，因此将显示器能耗降到最低具有重要的现实意义。这项研究的意义在于，这是一种从整个流媒体基础设施的角度出发，利用显示能量降低的新的整体方法。这种方法是对现有方案的范式转变，这些方案探索在每台移动设备上降低能源，并将为向移动设备传输视频开辟新的研究途径。这个高风险、高回报的项目具有比本提案中提出的研究更广泛和更具变革性的影响。首先，这项研究与以云为中心的信息技术基础设施的大趋势非常一致，并将为构建未来的视频流系统确立一个新的行业服务方向。其次，开发的算法、源代码和测量结果将在网上公开并广泛使用，使工业界和学术界的研究人员都受益。最后，对本科生和研究生的研究培训将培养出在信息技术和计算机行业具有多学科经验的研究人员和工程师。本研究旨在克服当前按设备处理实践的两个基本缺陷：(1)从全球角度来看，由于相同的计算必须在接收相同视频流的数千或数百万个移动设备之间重复，因此从全球角度来看，非常低效的节能。(2)设备级的非最佳显示能量降低，因为节省的显示能量和额外处理所需的能量之间的内在权衡阻碍了采用复杂的算法。这项研究的智能价值在于开发了一种范式转换设计，将每个设备的本地处理转移到基于数据中心的全局处理，并对视频内容进行智能编码，以实现高效显示流。特别是，本项目开展了两个主要的研究方向。为了将显示感知集成到视频流基础设施中，首先开发了数据中心的面向显示的视频处理，以利用视频源特征和不同类别的移动设备，以生成比特流系列，每个比特流具有嵌入的参数，用于在移动设备上降低显示能量。然后，移动接入网关处的情景感知视频适配被设计为对视频比特流进行代码转换，以便匹配设备观看环境和无线信道状态，以增强体验质量(QOE)。为了克服日益增长的视频流导致的巨大数据存储开销和视频缓存效率的下降，研究了一种基于语法的数据中心视频重复数据删除方法，以降低数据存储开销。然后，开发了一种数据中心辅助的网关通用转码方案，以降低接入网关的视频缓存未命中代价。最后，设计了一种针对视频缓存进行优化的固态数据存储方案，以充分挖掘闪存用于视频缓存的潜力。

Abstract: The objective of this research is to develop novel solutions that can systematically reduce the energy consumption of future video-rich mobile devices through re-inventing the entire video streaming infrastructure design. Minimizing display energy consumption is of great practical importance since it accounts for a very high percentage of mobile device energy consumption. The significance of this research is that this is a new holistic approach in which display energy reduction is exploited from the entire streaming infrastructure point of view. Such an approach is a paradigm shift from existing schemes that explore the energy reduction on each mobile device and shall open up new research avenues for video streaming to mobile devices. This high-risk, high-payoff project has broader and transformational impact beyond the research proposed in this proposal. First, this research is well aligned with the grand trend towards cloud-centric information technology infrastructure and will establish a new industrial service direction on architecting future video streaming systems. Second, the developed algorithms, source code, and measurement results will be made available online for public and wide-range usage, benefiting both industrial and academic researchers. Finally, the research training to both undergraduate and graduate students shall produce researchers and engineers with multidisciplinary experience in information technology and computer industries.This research aims at overcoming two fundamental drawbacks of current practice in per-device processing: (1) highly inefficient energy saving from global perspective since the same computation shall be repeated among thousands or millions of mobile devices that receive the same video stream. (2) Non-optimal display energy reduction at device level because the inherent trade-off between saved display energy and the energy needed for additional processing is preventing from adopting sophisticated algorithms. The intellectual merit of this research lies in the development of a paradigm shifting design to move the per-device local processing to data center-based global processing along with the intelligent encoding of the video content for display energy efficient streaming. In particular, two major research directions are carried out in this project. To integrate display awareness into the video streaming infrastructure, display-oriented video processing at data centers is developed first to exploit video source characteristics and different classes of mobile devices in order generate a family of bitstreams, each with embedded parameters for display energy reductions at mobile devices. Then, a context-aware video adaptation at mobile access gateways is designed to transcode the video bitstreams in order to match device viewing environments and wireless channel status for enhanced quality of experience (QoE). To overcome increasing number of video streams leading to significant data storage overhead and degradation of video caching efficiency, a syntax-aware video data deduplication at data centers is investigated to reduce the data storage overhead. Then, a data center assisted universal transcoding at gateways is developed to reduce video cache miss penalty at access gateways. Finally, a solid-state data storage scheme optimized for video caching is designed to fully exploit the potential of using flash memory for video caching.