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Risk-aware contextual learning for edge-assisted crowdsourced live streaming

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journal contribution
posted on 2023-02-02, 11:06 authored by Xingchi Liu, Mahsa DerakhshaniMahsa Derakhshani, Lyudmila Mihaylova, Sangarapillai LambotharanSangarapillai Lambotharan
This paper proposes an edge-assisted crowdsourced live video transcoding approach where the transcoding capabilities of the edge transcoders are unknown and dynamic. The resilience and trustworthiness of highly unstable transcoders in decision making are characterized with mean-variance-based measures to avoid making highly risky decisions. The risk level of each device’s situation is assessed and two upper confidence bounds of the variance of transcoding performance are presented. Based on the derived bounds and by leveraging the contextual information of devices, two risk-aware contextual learning schemes are developed to efficiently estimate the transcoding capabilities of the edge devices. Combining context awareness and risk sensitivity, a novel transcoding task assignment and viewer association algorithm is proposed. Simulation results demonstrate that the proposed algorithm achieves robust task offloading with superior network utility performance as compared to the linear upper confidence bound and the risk-aware mean-variance upper confidence bound-based algorithms. In particular, an epoch-based task assignment strategy is designed to reduce the task switching costs incurred in assigning the same transcoding task to different transcoders over time. This strategy also reduces the computational time needed. Numerical results confirm that this strategy achieves up to 86.8% switching costs reduction and 92.3% computational time reduction.

Funding

Royal Academy of Engineering under the Leverhulme Trust Research Fellowship scheme (Derakhshani-LTRF1920\16\67)

Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)

Engineering and Physical Sciences Research Council

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NSF-EPSRC:ShiRAS. Towards Safe and Reliable Autonomy in Sensor Driven Systems.

Engineering and Physical Sciences Research Council

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ECCS-EPSRC - ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems

Directorate for Engineering

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History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Journal on Selected Areas in Communications

Volume

41

Issue

3

Pages

740 - 754

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© The Authors

Publisher statement

For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) to any Accepted Manuscript version arising.

Acceptance date

2022-10-25

Publication date

2022-12-21

Copyright date

2022

ISSN

0733-8716

eISSN

1558-0008

Language

  • en

Depositor

Dr Mahsa Derakhshani. Deposit date: 1 February 2023