CSR: Small: A Unified Approach Toward User-specific Improvements of Quality of Experience for Video Streaming

CSR：小：针对特定用户改进视频流体验质量的统一方法

• 批准号: 1618931
• 负责人: Yao Liu
• 金额: $ 48.84万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618931&HistoricalAwards=false
• 关键词: CSR; Small; Unified; Approach; Toward

The number of users streaming mobile video over the Internet has increased at an unprecedented rate throughout the past decade. Because video streaming is a mainstay of mobile computing, it is important that the best possible experience is delivered to users. Many mathematical algorithms have been proposed to improve quality in video-streaming-related domains. Typically, the parameters of these algorithms are established based on machine-centric indicators of video quality. Although researchers have attempted to connect these indicators with true user-perceived quality, in practice, there is often a disconnect. This project aims to improve directly-measurable indicators of user satisfaction in mobile video streaming by taking into account both individual user preferences as well as a user's tolerance for less than perfect quality in a specific video. The proposed research will improve user-specific indicators by connecting problems in video streaming with problems in multi-task learning and collaborative filtering. These machine learning strategies are especially effective for prediction when large amounts of data are available. This effectiveness on large-scale learning fits well into this project's proposed context of improving user-facing indications of satisfaction. These indications include video abandonment, video session times, and collected navigation commands. Unlike user-surveys, these metrics can be collected at large scales through automated tooling in the video player. This research will investigate strategies that combine these large scale measurements with predictions from machine learning approaches toward selecting algorithm parameters that produce the most improvement in user-facing quality. This project will explore such parameter selection strategies in the context of improving user-perceived dynamic adaptive streaming quality. It will also explore such strategies to maintain a fixed level of user-facing quality while reducing mobile display power consumption via backlight scaling. Demonstrations of the approaches produced by this project will be featured in courses at the PI's institution and will be used to draw undergraduate interest toward computer science research.

在过去的十年中，通过Internet流动视频的用户数量以空前的速度增加。由于视频流是移动计算的主要支柱，因此必须将最佳体验交付给用户很重要。已经提出了许多数学算法来提高与视频流有关的域的质量。通常，这些算法的参数是基于以机器为中心的视频质量指标来建立的。尽管研究人员试图将这些指标与真正的用户感知质量联系起来，但实际上，通常会脱节。该项目旨在通过考虑单个用户的偏好以及用户在特定视频中的质量不足的耐受性来提高移动视频流中用户满意度的直接测量指标。拟议的研究将通过将视频流中的问题与多任务学习和协作过滤中的问题联系起来来改善用户特定的指标。当有大量数据可用时，这些机器学习策略对于预测特别有效。对大规模学习的有效性非常适合该项目提出的改善面向用户满意的迹象的背景。这些指示包括视频放弃，视频会话时间和收集的导航命令。与用户疗法不同，这些指标可以通过视频播放器中的自动工具在大规模上收集。这项研究将调查将这些大规模测量结果与从机器学习方法进行的预测相结合的策略，以选择算法参数，从而产生面向用户的质量最大的进步。该项目将在改善用户感知的动态自适应流质量的背景下探索这种参数选择策略。它还将探索此类策略，以维持固定水平的面向用户的质量，同时通过背光缩放降低移动显示功率消耗。该项目生产的方法的演示将在PI机构的课程中进行，并将用于吸引本科对计算机科学研究的兴趣。