A Segment-based Storage and Transcoding Trade-off Strategy for Multi-version VoD Systems in the Cloud
Multi-version VoD providers either store multiple
versions of the same video or transcode video to multiple
versions in real-time to offer multiple-bitrate streaming services
to heterogeneous clients. However, this could incur tremendous
storage cost or transcoding computation cost. There have been
some works regarding trading-off between transcoding and
storing whole videos, but they did not take into account video
segmentation and internal popularity. As a result, they were
not cost-efficient. This paper introduces video segmentation and
proposes a segment-based storage and transcoding trade-off
strategy for multi-version VoD systems in the cloud. First, we split
each video into multiple segments depending on the video internal
popularity. Second, we describe the transcoding relationships
among versions using a transcoding weighted graph, which can
be used to calculate the version-aware transcoding cost from
one version to another. Third, we take the video segmentation,
version-aware transcoding weighted graph, and video internal
popularity into account to propose a storage and transcoding
trade-off strategy, which stores multiple versions of popular
segments and transcodes unpopular segments. We then formulate
it as an optimization problem and present a heuristic divideand-conquer
algorithm to get an approximate optimal solution.
Finally, we conduct extensive simulations to evaluate the solution;
the results show that it can significantly lower the storage and
transcoding cost of multi-version VoD systems.
KeyWords
Multi-version VoD; segment-based; transcoding
weighted graph; storage and transcoding trade-off; cloud.
|