November 7, 2014

A Dataset for Cloud Live Rate-Adaptive Video

There is an audience for non-professional video "broadcasters", like gamers, online courses teachers and witnesses of public events. To meet this demand, live streaming service providers such as ustream, livestream, twitch or dailymotion have to find a solution for the delivery of thousands of good quality live streams to millions of viewers who consume video on a wide range of devices (from smartphone to HDTV). Yet, in current live streaming services, the video is encoded on the computer of the broadcaster and streamed to the data-center of the service provider, which in most cases chooses to simply forward the video it get from the broadcaster. The problem is that many viewers cannot properly watch the streams due to mismatches between encoding video parameters (i.e. video rate and resolution) and features of viewers’ connections and devices (i.e. connection bandwidth and device display).

To address this issue, adaptive streaming working along with cloud computing could be the answer. Whereas adaptive streaming allows managing the diversity of end-viewers requirements by encoding several video representations at different rates and resolutions, cloud computing provides the CPU resources to live transcode all these alternate representations from the broadcaster-prepared raw video.

It is well known that the QoE of an end-viewer watching a stream depends on the encoded video and the parameters values used in the transcoding. But, in this new scenario in the cloud, we also need to consider the transcoding CPU requirements. In the “cloud video” era, the selection of video encoding parameters should take into account not only the client (for the QoE), but also the data-center (for the allocated CPU). To set the video transcoding parameters, the cloud video service provider should know the relations among transcoding parameters, CPU resources and end-viewers QoE, ideally for any kind of video encoded on the broadcaster side.

We would like to announce the publication of a dataset containing CPU and QoE measurements corresponding to an extensive battery of transcoding operations in with the purpose of contributing to research in this topic. Most of the credits for this work (and so this post) have to be given to Ramon Aparicio-Pardo.

To elaborate the dataset, we have used four types of video content, four resolutions (from 224p up to 1080p) and bit rates values ranging from 100 kbps up to 3000 kbps. Initially, we have encoded each of the four video streams into 78 different combinations of rates and resolutions, emulating the encoding operations at the broadcaster side. Then, we transcode each of these broadcaster-prepared videos into all the representations with lower resolutions and bit rates values than the original one. The overall number of these operations, representing the cloud-transcoding, was 12168. For each one of these operations, we have measured the CPU cycles required to generate the transcoded representation and we have estimated the end-viewers’ satisfaction using the Peak Signal to Noise Ratio (PSNR) score). We depict a basic sketch of these operations for one specific case where the broadcaster encoded its raw video with 720p resolution at 2.25 Mbps and we transcode it into a 360p video at 1.6Mbps.

We give below an appetizer of how these CPU cycles and satisfaction decibels vary with transcoding parameters. They show some examples of the kind of results that you will find in the dataset, here a broadcaster-prepared video of type “movie,” 1080p resolution and encoded at 2750 kbps. If you wonder how the rest of figures look like, 558 curves and their corresponding 12168 measurements of cycles of hard CPU work and decibels of viewers’ satisfaction are waiting for you in

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