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- GAIKWAD Koustubh B.
- Graduate School of Science and Technology, Keio University
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- OHTSUKI Tomoaki
- Dept. of Information and Computer Science, Keio University
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説明
Live media streaming has become one of the most important applications over the Internet. However, having an all-round scalable, reliable, responsive and cost-effective solution for delivery of live video over multiple platforms is still a challenge. To deliver live video over a variety of platforms like laptops, mobile phones, tablets, gaming consoles, etc., the video needs to be encoded to appropriate format based on the device on which it is to be rendered. This is due to the heterogeneity and limited computing resources present in the devices. The traditional way of using hardware encoder has been known to be expensive and inefficient. Cloud provides virtually infinite on-demand resources for encoding and streaming. The main idea of this proposal is to vary the number of encoding/transcoding and streaming servers dynamically based on the user demand for each type of stream. We proposed a framework to deliver live video over multiple platforms and used Artificial Neural Networks (ANN) to predict user demand in [1]. This report is an extension to [1]. In this report, we formulate the resource allocation problem as a Multi-objective Integer Linear Programming (ILP) problem based on ANN predictions.
収録刊行物
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- 映像情報メディア学会技術報告
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映像情報メディア学会技術報告 39.20 (0), 13-16, 2015
一般社団法人 映像情報メディア学会
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詳細情報 詳細情報について
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- CRID
- 1390001204529919744
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- NII論文ID
- 110009978852
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- NII書誌ID
- AN1059086X
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- ISSN
- 24241970
- 13426893
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- NDL書誌ID
- 026683596
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可