Understanding the impact of video quality on user engagement

Florin Dobrian, V. Sekar, Asad Awan, I. Stoica, D. Joseph, Aditya Ganjam, Jibin Zhan, Hui Zhang
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引用次数: 767

Abstract

As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events. We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1% increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content.
了解视频质量对用户粘性的影响
随着视频在互联网上的传播成为主流,视频消费从电脑向电视屏幕转移,用户对视频高质量的要求也在不断提高。在这种情况下,对于内容提供商来说,了解视频质量是否以及如何影响用户参与度,以及如何最好地投入资源来优化视频质量是至关重要的。本文是解决这些问题的第一步。我们使用了一个独特的数据集,涵盖了不同的内容类型,包括短视频点播(VoD)、长视频点播(VoD)和流行视频内容提供商的直播内容。使用客户端检测,我们测量质量指标,如连接时间、缓冲比率、平均比特率、呈现质量和缓冲事件率。我们在每个视频(或观看)水平和每个用户(或观看者)水平上量化用户参与度。特别是,我们发现用于缓冲的时间百分比(缓冲比率)对所有类型内容的用户粘性影响最大。然而,这种影响的程度取决于内容类型,直播内容受到的影响最大。例如,对于一个90分钟的实时视频事件,缓冲比率增加1%可能会减少3分钟以上的用户粘性。我们还看到,在直播内容中,平均比特率比视频点播内容起着更重要的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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