Network characteristics of video streaming traffic

Video streaming represents a large fraction of Internet traffic. Surprisingly, little is known about the network characteristics of this traffic. In this paper, we study the network characteristics of the two most popular video streaming services, Netflix and YouTube. We show that the streaming strategies vary with the type of the application (Web browser or native mobile application), and the type of container (Silverlight, Flash, or HTML5) used for video streaming. In particular, we identify three different streaming strategies that produce traffic patterns from non-ack clocked ON-OFF cycles to bulk TCP transfer. We then present an analytical model to study the potential impact of these streaming strategies on the aggregate traffic and make recommendations accordingly.

[1]  V. Jacobson,et al.  Congestion avoidance and control , 1988, SIGCOMM '88.

[2]  Philippe Owezarski,et al.  A flow-based model for internet backbone traffic , 2002, IMW '02.

[3]  Ben Y. Zhao,et al.  Understanding user behavior in large-scale video-on-demand systems , 2006, EuroSys.

[4]  Zongpeng Li,et al.  Youtube traffic characterization: a view from the edge , 2007, IMC '07.

[5]  Pablo Rodriguez,et al.  I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system , 2007, IMC '07.

[6]  Cheng Huang,et al.  Can internet video-on-demand be profitable? , 2007, SIGCOMM '07.

[7]  Sonia Fahmy,et al.  Analyzing video services in Web 2.0: a global perspective , 2008, NOSSDAV.

[8]  Louis Plissonneau Revisiting web traffic from a DSL provider perspective : the case of YouTube , 2008 .

[9]  Anja Feldmann,et al.  On dominant characteristics of residential broadband internet traffic , 2009, IMC '09.

[10]  Michael Zink,et al.  Characteristics of YouTube network traffic at a campus network - Measurements, models, and implications , 2009, Comput. Networks.

[11]  Farnam Jahanian,et al.  Internet inter-domain traffic , 2010, SIGCOMM '10.

[12]  Marco Mellia,et al.  YouTube everywhere: impact of device and infrastructure synergies on user experience , 2011, IMC '11.

[13]  Richard Nelson,et al.  Application flow control in YouTube video streams , 2011, CCRV.

[14]  Ali C. Begen,et al.  An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP , 2011, MMSys.