High-Speed Distributed Video Transcoding for Multiple Rates and Formats

Sambe, Yasuo; Watanabe, S.; Yu, Dong Sik; Nakamura, Taichi; Wakamiya, Naoki

doi:10.1093/ietisy/e88-d.8.1923

Cited by 25 publications

(15 citation statements)

References 7 publications

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“…For the case of Open-GOP, in which there exists dependency between GOPs, we duplicate one GOP after segmentation point. This technique had been introduced in [4]. The difference is the fact that we do not limit the length of segments.…”

Section: Video Steam Structure and Splitting Algorithmmentioning

confidence: 99%

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An openstack-based flexible video transcoding framework in live

Shi¹,

Song²

2017

AIP Conference Proceedings

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Abstract.With the rapid development of mobile live business, transcoding HD video is often a challenge for mobile devices due to their limited processing capability and bandwidth-constrained network connection. For live service providers, it's wasteful for resources to delay lots of transcoding server because some of them are free to work sometimes. To deal with this issue, this paper proposed an Openstack-based flexible transcoding framework to achieve real-time video adaption for mobile device and make computing resources used efficiently. To this end, we introduced a special method of video stream splitting and VMs resource scheduling based on access pressure prediction,which is forecasted by an AR model.

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Section: Video Steam Structure and Splitting Algorithmmentioning

confidence: 99%

“…There have been some research efforts devoted to cloud computing. [4] and [5] divides the video file into small segment and transcodes them in parallel. Akamai's [6] Content Distribution Network (CDN) is able to achieve better load balancing and higher throughput, but it is expensive and not flexible enough.…”

Section: Introductionmentioning

confidence: 99%

An openstack-based flexible video transcoding framework in live

Shi¹,

Song²

2017

AIP Conference Proceedings

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Section: System Architecturementioning

confidence: 99%

“…[4] and [5] divide the video file into small segments and transcodes them in parallel. They focused on handling the length of segments to optimize the transcoding speed.…”

Section: Introductionmentioning

confidence: 99%

Parallelizing video transcoding using Map-Reduce-based cloud computing

Lao

Zhang

Guo

2012

2012 IEEE International Symposium on Circuits and Systems

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Due to the complexity of video coding, fast transcoding is still a challenge. Various parallel coding methods have been proposed. In this paper, we present a parallel transcoding system over Map/Reduce cloud computing architecture. Input video sequences are divided into segments, and mapped to multiple computers. The sub-tasks are launched in parallel with processing results concatenated to the final output sequences. For heterogeneous clips, computing capacity, and task-launching overhead, the task scheduling over cloud is an NP-hard problem. We propose a low-complexity heuristic algorithm, Max-MCT, to find out the optimal solutions for task scheduling. By estimating the low-bound of finish time, we transform the problem into a virtual knapsack problem. But it is not an optimal solution for the original problem therefore we use a minimal complete time (MCT) algorithm to minimize the entire finish time. We carry out extensive experiments on numerical simulations. The results verified that our algorithm outperforms the existing algorithms.

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“…Reference [2] illustrates that the network-based video transcoding is feasible. Reference [3] discusses the cluster transcoding implementation technology.…”

Section: Introductionmentioning

confidence: 99%

The Design and Implementation of Job Management System Based on Feedback Control

Cao

Lu²,

Zhu

2010

2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science

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We design and implement a Job Management System for improving automation level of job execution process in network-based digital multimedia content production. Jobs are automatically parallelized and executed on a large number of execution machines. Our implementation of the Job Management System has two novel aspects. One aspect is using a feedback mechanism in execution time estimation base on Extreme Learning Machine (ELM) for self-optimizing. Based on the feedback mechanism, we can continually use the newly obtained data to improve the system. The other one is having the capability of autonomic fault recovery. The system has been integrated to a network-based digital multimedia content production platform.

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High-Speed Distributed Video Transcoding for Multiple Rates and Formats

Cited by 25 publications

References 7 publications

An openstack-based flexible video transcoding framework in live

An openstack-based flexible video transcoding framework in live

Parallelizing video transcoding using Map-Reduce-based cloud computing

The Design and Implementation of Job Management System Based on Feedback Control

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