Adaptive Quality Video Streaming Optimization: An Intelligent Multi-Neural Framework for Enhanced Quality of Experience in 5G and Beyond Networks

Summary

This article presents the AQVSO (Adaptive Quality Video Streaming Optimization) framework as an integration of advanced neural and optimization models designed to enhance video streaming in 5G and next-generation networks. The framework improves video quality, reduces buffering, and optimizes resource use.

Abstract

AQVSO (Adaptive Quality Video Streaming Optimization) is a new video streaming framework that aims to enhance video streaming quality and resource utilization in 5G & Beyond networks. The study proposes an end-to-end multi-neural network comprising four co-related modules that incorporate sparse convolutional networks with policy-driven encoders (SCN-PDE), a sparse graph attention convolutional network (SGA-ConvNet), adaptive spiking neural networks (ASNN), and deep belief networks with ant colony optimization (DBN-ACO). These elements dynamically adjust to network changes based on a mathematical model that aims to strike a balance between quality and resource use. Experiments on the UCF-101 dataset show that AQVSO preserves SSIM scores similar to those of state-of-the-art algorithms BBA and BOLA (0.977), while requiring much less bandwidth (4,936/8,000 kbps). The framework can save 38% in bandwidth consumption while maintaining high perceptual quality (PSNR = 45.89 dB) and has nearly eliminated buffering occurrences. The system achieves 99.9% streaming certainty under mobile, CDN (Content Delivery Network), and enterprise conditions, providing an actual performance gain for video delivery systems in resource-limited situations. This is made possible by providing a manageable user experience and efficient use of network resources through adaptive , content-aware streaming decisions.

Introduction

The objective of this study is to develop an integrated multi-neural adaptive streaming framework, termed AQVSO, that jointly optimizes perceptual video quality and bandwidth utilization under dynamic 5G and beyond network conditions. The method aims to unify content analysis, rate control, and resource allocation into a single decision architecture to achieve consistent Quality of Experience (QoE) at significantly lower bitrate requirements.

Adaptive Quality Video Streaming Optimization (AQVSO)

In the era of 5G and beyond, AQVSO is focusing on improving video streaming quality-of-experience (QoE....

Protocol

The software used is listed in the Table of Materials.

Dataset setup

The UCF-101³ dataset was obtained and extracted into the Videos/UCF-101 directory. Dataset integrity was assessed through checksum verification or manual playback, and any corrupted files were excluded from subsequent processing.

Computational environment

All experiments were performed in Python 3.10 on Windows 10/11 or Ubuntu 20.04+. The software environment included tensorflow==2.17.0, opencv-python, ....

Results

Present a thorough analysis of the performance of the proposed system and report the implementation results. Include a comparison section to confirm that the system is appropriate for Neuro Cloud Stream, a cloud-based ML approach. Figure 2 illustrates the AQVSO system workflow.

Dataset description

To evaluate the AQVSO framework, use a diverse collection of video content ranging from 10 s to 2 h and 30 min. The comprehe.......

Discussion

The Adaptive Quality Video Streaming Optimization (AQVSO) framework is a multimedia processing engine based on an embedded multi-module codec integration architecture to dynamically enhance perceptual video quality and bitrate adaptivity in wireless networks. Unlike individual predictors or rate controllers, for example, AQVSO utilizes SCN-PDE to enhance motion-awareness, SGA-ConvNet for spatiotemporal attention modeling, ASNN for low-latency adaptive rate control, and DBN-ACO for globally optimized bit allocation. This .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
8 GB RAM	Generic		Minimum memory required for running the experiments.
Intel Core i5 Processor	Intel Corporation		CPU used for AQVSO evaluation pipeline.
NumPy	NumPy Developers		Numerical computations and matrix operations.
OpenCV-Python	OpenCV.org		Used for video loading, frame extraction, and preprocessing.
Pandas	Pandas Development Team		Data handling, CSV export, metric tables.
Python 3.10 (Programming Language)	Python Software Foundation		Required for executing the AQVSO framework scripts and dependent libraries.
scikit-image	Scikit-Image Developers		SSIM, PSNR, MSE computation.
TensorFlow 2.17.0	Google		Deep learning library used for building neural modules (SCN, SGA-ConvNet, ASNN, DBN).
tqdm	TQDM Team		Progress bars for pipeline execution.
Windows 10 (64-bit) Operating System	Microsoft Corporation		Operating system used for experiments.