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

  • 0Department of Electrical, Electronics, and Communications Engineering, GITAM School of Technology, GITAM Deemed to Be University.

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Summary

This summary is machine-generated.

Adaptive Quality Video Streaming Optimization (AQVSO) significantly reduces bandwidth use by 38% while maintaining high video quality. This novel framework ensures 99.9% streaming certainty, minimizing buffering for an improved user experience.

Area Of Science

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background

  • Current video streaming frameworks face challenges in balancing quality with resource utilization, especially in 5G & Beyond networks.
  • Efficiently managing bandwidth and ensuring consistent playback quality are critical for user satisfaction and network performance.

Purpose Of The Study

  • To introduce Adaptive Quality Video Streaming Optimization (AQVSO), a novel framework designed to enhance video streaming quality and resource efficiency.
  • To dynamically adapt streaming parameters to network conditions, optimizing both video quality and resource consumption.

Main Methods

  • Development of an end-to-end multi-neural network framework (AQVSO).
  • Integration of four co-related modules: Sparse Convolutional Networks with Policy-Driven Encoders (SCN-PDE), Sparse Graph Attention Convolutional Network (SGA-ConvNet), Adaptive Spiking Neural Networks (ASNN), and Deep Belief Networks with Ant Colony Optimization (DBN-ACO).
  • Utilization of a mathematical model for dynamic adjustment to network changes, balancing quality and resource use.

Main Results

  • AQVSO achieved Structural Similarity Index (SSIM) scores of 0.977, comparable to state-of-the-art algorithms BBA and BOLA.
  • Demonstrated a 38% reduction in bandwidth consumption (4,936/8,000 kbps) while maintaining high Peak Signal-to-Noise Ratio (PSNR) of 45.89 dB.
  • Achieved nearly eliminated buffering occurrences and 99.9% streaming certainty across mobile, CDN, and enterprise networks.

Conclusions

  • AQVSO offers a significant performance gain for video delivery systems, particularly in resource-limited environments.
  • The framework provides an improved user experience through adaptive, content-aware streaming decisions.
  • AQVSO effectively balances high perceptual video quality with efficient network resource utilization.

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