Transcoding Live Adaptive Video Streams at a Massive Scale in the Cloud

Abstract : More and more users are watching online videos produced by non-professional sources (e.g., gamers, teachers of online courses, witnesses of public events) by using an increasingly diverse set of devices to access the videos (e.g., smartphones, tablets, HDTV). Live streaming service providers can combine adaptive streaming technologies and cloud computing to satisfy this demand. In this paper, we study the problem of preparing live video streams for delivery using cloud computing infrastructure, e.g., how many representations to use and the corresponding parameters (resolution and bit-rate). We present an integer linear program (ILP) to maximize the average user quality of experience (QoE) and a heuristic algorithm that can scale to large number of videos and users. We also introduce two new datasets: one characterizing a popular live streaming provider (Twitch) and another characterizing the computing resources needed to transcode a video. They are used to set up realistic test scenarios. We compare the performance of the optimal ILP solution with current industry standards, showing that the latter are sub-optimal. The solution of the ILP also shows the importance of the type of video on the optimal streaming preparation. By taking advantage of this, the proposed heuristic can efficiently satisfy a time varying demand with an almost constant amount of computing resources.
Keywords : Live streaming
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  • HAL Id : hal-01185076, version 1

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Ramon Aparicio-Pardo, Karine Pires, Alberto Blanc, Gwendal Simon. Transcoding Live Adaptive Video Streams at a Massive Scale in the Cloud. MMSys 2015 : 6th ACM Multimedia Systems Conference, Mar 2015, Portland, United States. pp.49 - 60. ⟨hal-01185076⟩

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