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Related Experiment Video

Updated: Aug 14, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Published on: December 15, 2023

Video coding deep learning-based modeling for long life video streaming over next network generation.

Mohammad Alsmirat^1,2, Yousef Sharrab^3,4, Monther Tarawneh³

¹Computer Science Department, University of Sharjah, Sharjah, UAE.

Cluster Computing

|January 9, 2023

Summary

This study uses deep learning to predict video streaming quality, power, and bandwidth for H.264/AVC codecs. Accurate predictions enhance smart network availability under heavy video traffic.

Keywords:

Artificial neural networks Deep learning Encoding power consumption modeling Machine learning Perceptual video quality modeling Video communication Video communication systems Video streaming

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Area of Science:

Computer Science
Electrical Engineering

Background:

Smart network availability is crucial, especially during high video streaming traffic.
Video streaming systems require efficient resource management for optimal performance.

Purpose of the Study:

To propose a deep learning methodology for predicting video streaming quality, power consumption, and bandwidth.
To enhance the availability of video streaming systems in smart networks.

Main Methods:

Developed a deep learning prediction model for video streaming metrics.
Utilized H.264/AVC codec parameters (resolution, quantization) as input.
Trained, validated, and tested models using extensive experimental data.

Main Results:

Accurate prediction models were built for video streaming quality, power consumption, and bandwidth.
The models demonstrated high accuracy in predicting key video streaming parameters.
The methodology proved effective in enhancing network availability.

Conclusions:

Deep learning models can accurately predict video streaming quality, power, and bandwidth.
These predictions are valuable for improving network availability in cooperative environments.
The proposed methodology offers a robust solution for managing video streaming resources.