Abstract
Video streaming is one of the killer applications in recent years. Video transcoding plays an important role in the video streaming service to cope with the various purposes. Specifically, content owners and publishers heavily utilize video transcoders to reconfigure source video in a variety of formats, video qualities, and bitrate to provide end users with the best possible quality of service. In this paper, we present VideoCoreCluster, a low-cost and energy-efficient transcoder cluster that is suitable for live streaming services. We designed and implemented real-time video transcoder cluster using cheap ($35), powerful, and energy-efficient Raspberry Pi. The quality of transcoded video provided by VideoCoreCluster is similar to the best software-based video transcoder while consuming significantly less energy (<3 W). We have proposed a scheduling algorithm based on priority of video stream and transcoding capacity. Our cluster manager provides reliable and scalable streaming services, because it uses the characteristics of adaptive bitrate scheme. We have deployed our transcoding cluster to provide IP-based TV streaming services on our university campus.
Highlights
Video streaming service is one of the most popular Internet services in recent years
Adaptive bitrate (ABR) server must ensure that all video variants are available when user requests any of them and switches to other variants without pausing
Several optimization techniques are not applicable for live streaming, because of the strict requirements for low latency. It has to ensure the variants of video segments are generated synchronously to simplify the implementation of ABR algorithms
Summary
Video streaming service is one of the most popular Internet services in recent years. Video content owners and distributors need to encode video in various formats, qualities, and bitrates to provide high and robust video streaming service quality to end users under varying network conditions. It is difficult for video service providers to prepare media contents in a variety of formats due to numerous bitrates, codecs, and formats. Several optimization techniques (e.g., high-throughput video transcoding, multipass encoding) are not applicable for live streaming, because of the strict requirements for low latency. It has to ensure the variants of video segments are generated synchronously to simplify the implementation of ABR algorithms. Due to the reasons mentioned above, ABR technique is challenging to support live streams
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