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

在过去十年中，通过互联网传输移动视频的用户数量以前所未有的速度增长。由于视频流是移动计算的支柱，因此向用户提供最佳体验非常重要。为了提高视频流相关领域的质量，已经提出了许多数学算法。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，但在实践中，往往存在脱节。该项目旨在通过考虑个人用户偏好以及用户对特定视频质量不完美的容忍度，改善移动视频流中用户满意度的直接可测量指标。提出的研究将通过将视频流中的问题与多任务学习和协同过滤中的问题联系起来，改进用户特定指标。当有大量数据可用时，这些机器学习策略对预测特别有效。这种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的满意度指标的背景。这些指示包括视频放弃、视频会话时间和收集的导航命令。与用户调查不同，这些指标可以通过视频播放器中的自动化工具大规模收集。本研究将研究将这些大规模测量与机器学习方法的预测相结合的策略，以选择算法参数，从而最大程度地提高面向用户的质量。本项目将在提高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。它还将探索这样的策略，以保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耗。该项目所产生的方法的演示将在PI所在机构的课程中出现，并将用于吸引本科生对计算机科学研究的兴趣。

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.