Budget-aware Video Crowdsourcing at the Cloud-enhanced Mobile Edge

Siqi Huang, Xueqing Huang, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

Abstract

With convenient Internet access and ubiquitous high-quality sensors in end-user devices, a growing number of content consumers are engaging in the content creation process. Meanwhile, mobile edge computing (MEC) can provision distributed computing resources for local data processing. The MEC-enhanced video crowdsourcing application will gather user-generated video contents and collectively distribute them to the viewers of interest. To empower the crowdsourced video streaming at the edge, we investigate how to efficiently transmit data from content generators to the viewers. In particular, for a group of collaborative mobile users willing to share their data with the viewers, the content generation and delivery scheme is designed by considering the cost incurred by the crowdsourcing application. By leveraging the cloud resources available at the wireless base stations, the uploading or downloading server site is chosen for each user. To minimize the system makespan, i.e., the overall data transmission time among the generators and viewers, the user association scheme is also designed to efficiently utilize the diverse wireless radio resources. As compared with traditional centralized/distributed content delivery schemes, the proposed algorithm can improve the cost-effectiveness of distributed radio/cloud resources deployed at the mobile edge.

Original languageEnglish (US)
JournalIEEE Transactions on Network and Service Management
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Cloud computing
  • Crowdsourcing
  • Generators
  • MEC-enhanced video crowdsourcing
  • Power demand
  • Quality of service
  • server placement
  • Servers
  • Streaming media
  • system makespan
  • user association.

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