ReCLive: Real-Time Classification and QoE Inference of Live Video Streaming Services

被引：4
|
作者：
Madanapalli, Sharat Chandra [1 ]
Mathai, Alex [2 ]
Gharakheili, Hassan Habibi [1 ]
Sivaraman, Vijay [1 ]
机构：
[1] UNSW, Sch Elect Engn & Telecommun, Sydney, NSW, Australia
[2] BITS Pilani, Pilani, Rajasthan, India
来源：
2021 IEEE/ACM 29TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS) | 2021年
关键词：
traffic classification; video streaming; QoE inferencing; machine learning;
D O I：
10.1109/IWQOS52092.2021.9521288
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Social media, professional sports, and video games are driving rapid growth in live video streaming, on platforms such as Twitch and YouTube Live. Live streaming experience is very susceptible to short-time-scale network congestion since client playback buffers are often no more than a few seconds. Unfortunately, identifying such streams and measuring their QoE for network management is challenging, since content providers largely use the same delivery infrastructure for live and video-on-demand (VoD) streaming, and packet inspection techniques (including SNI/DNS query monitoring) cannot always distinguish between the two. In this paper, we design and develop ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics.
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页数：7
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