Abstract
YouTube is changing the way operators manage network performance monitoring. In this paper we introduce YOUQMON, a novel on-line monitoring system for assessing the Quality of Experience (QoE) undergone by HSPA/3G customers watching YouTube videos, using network-layer measurements only. YOUQMON combines passive traffic analysis techniques to detect stalling events in YouTube video streams, with a QoE model to map stallings into a Mean Opinion Score reflecting the end-user experience. We evaluate the stalling detection performance of YOUQMON with hundreds of YouTube video streams, and present results showing the feasibility of performing real-time YouTube QoE monitoring in an operational mobile broadband network.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Software
Cited by
34 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Automatic Detection of Stalling Events using Machine Learning Algorithms;INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL;2024-09-02
2. Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale;IEEE Transactions on Network and Service Management;2024-06
3. Identifying Video Resolution from Encrypted QUIC Streams in Segment-combined Transmission Scenarios;Proceedings of the 34th edition of the Workshop on Network and Operating System Support for Digital Audio and Video;2024-04-15
4. QoE Estimation for the Wi-Fi Edge with Gradient Boosting-based Machine Learning;2023 International Balkan Conference on Communications and Networking (BalkanCom);2023-06-05
5. QoE Evaluation in Adaptive Streaming: Enhanced MDT with Deep Learning;Journal of Network and Systems Management;2023-03-24