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Requet: Real-Time QoE Metric Detection for Encrypted YouTube Traffic

Published:10 July 2020Publication History
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Abstract

As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for REal-time QUality of experience metric detection for <underline>E</underline>ncrypted Traffic—Requet—which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.

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          • Published in

            cover image ACM Transactions on Multimedia Computing, Communications, and Applications
            ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 2s
            Special Issue on Smart Communications and Networking for Future Video Surveillance and Special Section on Extended MMSYS-NOSSDAV 2019 Best Papers
            April 2020
            291 pages
            ISSN:1551-6857
            EISSN:1551-6865
            DOI:10.1145/3407689
            Issue’s Table of Contents

            Copyright © 2020 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 10 July 2020
            • Online AM: 7 May 2020
            • Accepted: 1 April 2020
            • Revised: 1 March 2020
            • Received: 1 December 2019
            Published in tomm Volume 16, Issue 2s

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