Published February 20, 2026 | Version v1
Software Open

X4-Match: Sustainable Prediction-based Video Encoding Scheduling Framework

  • 1. ROR icon University of Klagenfurt
  • 2. Salzburg research
  • 3. ROR icon Baylor University

Description

This framework provides a sustainable, energy-efficient scheduling method that integrates ML–based prediction with game-theoretic heuristics to optimize video encoding workloads across cloud and edge instances.

 

BibTex:
@inproceedings{afzal2026x4,
  title={X4-MATCH: Sustainable Prediction-based Distribution of Video Encoding on Cloud and Edge},
  author={Afzal, Samira and Mehran, Narges and Freeman, Andrew C. and Hoi, Manuel and Lachini, Armin and Timmerer, Christian and Prodan, Radu},
  booktitle={40th IEEE International Parallel \& Distributed Processing Symposium, May 2026},
  year={2026}
}

Files

X4-MATCH.zip

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Additional details

Funding

Baylor University
The Freeman Multimedia Lab
Land Salzburg
EXDIGIT (Excellence in Digital Sciences and Interdisciplinary Technologies) 20204-WISS/263/6-6022
Christian Doppler Research Association
Adaptive Streaming over HTTP and Emerging Networked Multimedia Services (ATHENA)
Austrian Research Promotion Agency
AIM AT Stiftungsprofessur für Edge AI 909989

Software

Programming language
Python , Jupyter Notebook