Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming-Reference-Cited by-同舟云学术

Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming

Published:2023-03 Issue:3 Volume:41 Page:740-754
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Liu Xingchi¹^ORCID,Derakhshani Mahsa²^ORCID,Mihaylova Lyudmila¹^ORCID,Lambotharan Sangarapillai²^ORCID

Affiliation:

1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, U.K

2. Signal Processing and Networks Research Group, Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, U.K

Funder

Royal Academy of Engineering under the Leverhulme Trust Research Fellowship Scheme

Engineering and Physical Sciences Research Council (EPSRC) through “Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)”

“ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems”

USA National Science Foundation through ShiRAS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

https://ieeexplore.ieee.org/ielam/49/10044995/9996351-aam.pdf

Reference51 articles.

1. Coping With Heterogeneous Video Contributors and Viewers in Crowdsourced Live Streaming: A Cloud-Based Approach

2. OSCAR: On Optimizing Resource Utilization in Live Video Streaming

3. Crowdsourced Multi-View Live Video Streaming using Cloud Computing