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Reliable QoE Prediction in IMVCAs Using an LMM-Based Agent.

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  • 1School of Electrical and Computer Engineering, Ben Gurion University of the Negev, Be'er Sheba 8499000, Israel.

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Summary
This summary is machine-generated.

Internet Service Providers can now infer video call quality using machine learning. Analyzing WhatsApp traffic, this study accurately predicts Quality of Experience (QoE) metrics like BRISQUE, PIQE, and FPS.

Keywords:
Large Multimodal Modelsencrypted trafficmachine learningquality of experiencevideo conferencing

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Area of Science:

  • Computer Science
  • Telecommunications Engineering
  • Data Science

Background:

  • Video Conferencing (VC) applications are increasingly vital for communication.
  • Instant Messaging Video Call Applications (IMVCAs) dominate VC usage on mobile devices.
  • Accurate Quality of Experience (QoE) assessment is crucial for IMVCAs, yet challenging for Internet Service Providers (ISPs) due to encrypted traffic.

Purpose of the Study:

  • To develop and evaluate methods for ISPs to infer IMVCA Quality of Experience (QoE) from network traffic.
  • To analyze a large dataset of WhatsApp Instant Messaging Video Call Application (IMVCA) sessions.
  • To compare the performance of Machine Learning (ML) algorithms and a Large Multimodal Model (LMM) for QoE prediction.

Main Methods:

  • Collected and analyzed a dataset of over 25,000 seconds of WhatsApp IMVCA sessions.
  • Applied four distinct Machine Learning (ML) algorithms to the dataset.
  • Utilized a Large Multimodal Model (LMM)-based agent for QoE metric prediction.

Main Results:

  • Achieved a mean error of 4.61% for predicting the BRISQUE QoE metric.
  • Achieved a mean error of 5.36% for predicting the PIQE QoE metric.
  • Achieved a mean error of 13.24% for predicting the FPS QoE metric.

Conclusions:

  • Machine Learning and LMM-based approaches can effectively infer key Quality of Experience (QoE) metrics for encrypted Instant Messaging Video Call Application (IMVCA) traffic.
  • The proposed methods offer a viable solution for Internet Service Providers (ISPs) to monitor and manage video call quality.
  • Accurate QoE prediction is essential for maintaining user satisfaction in the growing landscape of video communication services.