As the distribution of video over the Internet is becoming mainstream, user expectation for higb quality is constantly increasing. In this context, it is crucial for content providers to understand if and bow video quality affects user engagement and bow to best invest tbeir resources to optimize video quality. Tbis paper is a first step toward addressing tbese questions. We use a unique dataset tbat spans different content types, including sbort video on demand (VoD), long VoD, and live content from popular video content providers. Using client-side instrumentation, we measure quality metrics sucb as tbe join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events. We find tbat tbe percentage of time spent in buffering (buffering ratio) bas tbe largest impact on tbe user engagement across all types of content. However, tbe magnitude of tbis impact depends on tbe content type, witb live content being tbe most impacted. For example, a 1% increase in buffering ratio can reduce user engagement by more tban 3 min for a 90-min live video event.
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