Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud | JoVE Visualize

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

S P Tamizhselvi¹, Vijayalakshmi Muthuswamy²

¹Department of Computer Science & Engineering, Faculty of Information & Communication Engineering, KCG College of Technology, Anna University, Chennai, India.

Peer-To-Peer Networking and Applications|May 10, 2021

View abstract on PubMed

Summary

This study introduces a novel mobile cloud computing framework to enhance video streaming quality by optimizing bandwidth estimation and intelligent video transcoding, significantly improving performance metrics.

Area of Science:

Computer Science
Mobile Computing
Cloud Computing

Background:

Mobile video streaming faces challenges with network parameters like bandwidth and delay.
Mobile Cloud Computing (MCC) integrates cloud services with smartphones to address these issues.
Existing solutions struggle with resource-intensive video processing tasks on mobile devices.

Purpose of the Study:

To propose a novel framework for delay-aware bandwidth estimation and intelligent video transcoding in mobile cloud environments.
To improve Quality of Service (QoS) for mobile video streaming.
To reduce video processing delay and transcoding workload.

Main Methods:

Developed four techniques: Markov Mobile Bandwidth Cloud Estimation (MMBCE), Cloud Dynamic Congestion Window (CDCW), Queue-based Video Processing for Cloud Server (QVPS), and Intelligent Video Transcoding for selecting Server (IVTS).
Utilized Markov Decision Process (MDP) for bandwidth estimation and M/M/S and M/G/N queueing models for delay reduction and workload management.
Implemented a testbed using mobile configurations and Amazon Web Services (AWS) for evaluation.

Main Results:

MMBCE improved Packet Delivery Ratio (PDR) by 50%.
CDCW increased goodput by 40% with minimal Packet Loss Rate (PLR).
QVPS and IVTS reduced video processing delay and transcoding workload, with queueing models decreasing delay by 0.2 ms and server utilization by 20%.

Conclusions:

The proposed framework effectively minimizes delay and enhances video streaming quality on mobile devices.
The integration of MCC with optimized bandwidth estimation and intelligent transcoding significantly improves QoS.
The study demonstrates the practical viability and superior performance of the proposed techniques in real-world environments.