Deep Learning Based Power Control for Quality-Driven Wireless Video Transmissions

TL;DR

This paper introduces a deep learning approach to power control in wireless video transmission, enabling real-time, energy-efficient quality management despite interference challenges.

Contribution

It proposes a novel deep neural network method to approximate optimal power control, reducing computational complexity for real-time wireless video quality assurance.

Findings

DNN-based power control achieves near-optimal performance.

Reduces computational complexity compared to traditional methods.

Enables real-time quality-driven wireless video transmission.

Abstract

In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions while using the energy resources efficiently, we assume that power control is employed. Due to the presence of interference, determining the optimal power control is a non-convex problem but can be solved via monotonic optimization framework. However, monotonic optimization is an iterative algorithm and can often entail considerable computational complexity, making it not suitable for real-time applications. To address this, we propose a learning-based approach that treats the input and output of a resource allocation algorithm as an unknown nonlinear mapping and a deep neural network (DNN) is employed to learn this mapping. This learned mapping via DNN can provide the optimal power level quickly for given channel conditions.