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Abstract
The widespread use of mobile devices propels the development of new-fashioned video applications like 3D (3-Dimensional) stereo video and mobile cloud game via web or App, exerting more pressure on current mobile access network. To address this challenge, we adopt the crowdsourcing paradigm to offer some incentive for guiding the movement of recruited crowdsourcing users and facilitate the optimization of the movement control decision. In this paper, based on a practical 4G (4th-Generation) network throughput measurement study, we formulate the movement control decision as a cost-constrained user recruitment optimization problem. Considering the intractable complexity of this problem, we focus first on a single crowdsourcing user case and propose a pseudo-polynomial time complexity optimal solution. Then, we apply this solution to solve the more general problem of multiple users and propose a graph-partition-based algorithm. Extensive experiments show that our solutions can improve the efficiency of real-time D2D communication for mobile videos.
Original language | English |
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Title of host publication | WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining |
Place of Publication | New York, NY |
Publisher | Association for Computing Machinery |
Pages | 1140-1148 |
Number of pages | 9 |
ISBN (Electronic) | 9781450391320 |
DOIs | |
Publication status | Published - 2022 |
Event | 15th ACM International Conference on Web Search and Data Mining, WSDM 2022 - Virtual Event, Tempe, United States Duration: 21 Feb 2022 → 25 Feb 2022 |
Conference
Conference | 15th ACM International Conference on Web Search and Data Mining, WSDM 2022 |
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Country/Territory | United States |
City | Tempe |
Period | 21/02/22 → 25/02/22 |
Keywords
- Mobile Videos
- D2D Communication
- Movement Control
- Utility Optimization
- Movement control
- D2d communication
- Mobile videos
- Utility optimization
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Dive into the research topics of 'Crowdsourcing-based multi-device communication cooperation for mobile high-quality video enhancement'. Together they form a unique fingerprint.Projects
- 1 Finished
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DE21 : Scalable and Deep Anomaly Detection from Big Data with Similarity Hashing
1/01/21 → 31/12/23
Project: Research