Video quality representation classification of encrypted HTTP adaptive video streaming

Ran Dubin, Ofer Hadar, Amit Dvir, Ofir Pele

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.

Original languageEnglish
Pages (from-to)3804-3819
Number of pages16
JournalKSII Transactions on Internet and Information Systems
Issue number8
StatePublished - 31 Aug 2018


  • Encrypted traffic
  • HTTPS video streaming
  • Machine learning
  • Quality representation classification
  • YouTube

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications


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