Title
A Segment-Based Storage and Transcoding Trade-off Strategy for Multi-version VoD Systems in the Cloud.
Abstract
Multi-version video-on-demand (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 divide-and-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.
Year
DOI
Venue
2017
10.1109/TMM.2016.2612123
IEEE Trans. Multimedia
Keywords
Field
DocType
Transcoding,Streaming media,Static VAr compensators,Bandwidth,Delays,Industries,Real-time systems
Transcoding,Video post-processing,Computer science,Popularity,Computer network,Artificial intelligence,Optimization problem,Computation,Computer vision,Heuristic,Segmentation,Multimedia,Cloud computing
Journal
Volume
Issue
ISSN
19
1
1520-9210
Citations 
PageRank 
References 
7
0.52
34
Authors
5
Name
Order
Citations
PageRank
Hui Zhao1199.94
Qinghua Zheng21261160.88
Weizhan Zhang310118.64
Biao Du470.52
Haifei Li531232.20