ABSTRACT
Youtube [1] has indicated that over 4 billion hours of videos are watched each month and 72 hours of video are uploaded every minute. Another study from Cisco [2] has indicated that the overall mobile data tra c reached 885 petabytes per month at the end of 2012, 51% of which are mobile video. Forecasts predict that mobile video will grow at a compound annual growth rate (CAGR) of 75% between 2012 and 2017 and reach at 1 exabyte per month by 2017. During video playback, mobile devices may encounter dierent wireless network conditions. e Dynamic Adaptive Streaming over Hypertext Transfer Protocol (HTTP; DASH or MPEG-DASH) [3] standard is designed to provide high-quality streaming of media content over the Internet delivered from conventional HTTP Web servers. e content, divided into a sequence of segments, is made available at a number of dierent bit rates so that an MPEG-DASH client can automatically select the next segment to download and play back based on the current network conditions. e task of transcoding media content to dierent qualities and bit rates is computationally expensive, especially in the context of large-scale video hosting systems. erefore, it is preferably executed in a powerful cloud environment, rather than on the source computer (which may be a mobile device with limited memory, CPU speed, and battery life). In order to support the live distribution of media events and to provide a satisfactory user experience, the overall processing delay of videos should be kept to a minimum. In this chapter, we describe and explore various scheduling techniques for DASH-compatible systems in the context of large-scale media distribution clouds.
