Analyzing the impact of YouTube delivery policies on user experience

This paper presents an in-depth study of YouTube video service delivery. We have designed a tool that crawls YouTube videos in order to precisely evaluate the quality of experience (QoE) as perceived by the user. We enrich the main QoE metric, the number of video stalls, with many network measurements and use multiple DNS servers to understand the main factors that impact QoS and QoE. This tool has been used in multiple configurations: first, to understand the main delivery policies of YouTube videos, then to understand the impact of the ISP on these policies and finally, to compare the US and Europe YouTube policies. Our main results are that: (i) geographical proximity does not matter inside Europe or the US, but link cost and ISP-dependent policies do; (ii) usual QoS metrics (RTT) have no impact on QoE (video stall); (iii) QoE is not impacted nowadays (with good access networks) by access capacity but by peering agreements between ISPs and CDNs, and by server load. We also indicate a network monitoring metric that can be used by ISPs to roughly evaluate the QoE of HTTP video streaming of a large set of clients at a reduced computational cost.

[1]  Guillaume Urvoy-Keller,et al.  On Profiling Residential Customers , 2011, TMA.

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

[3]  Wolfgang Mühlbauer,et al.  Comparing DNS resolvers in the wild , 2010, IMC '10.

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

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

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

[7]  Nick Feamster,et al.  Broadband internet performance , 2011, SIGCOMM 2011.

[8]  Anja Feldmann,et al.  Improving content delivery using provider-aided distance information , 2010, IMC '10.

[9]  Ernst W. Biersack,et al.  A longitudinal view of HTTP video streaming performance , 2012, MMSys '12.

[10]  Zhi-Li Zhang,et al.  Where Do You "Tube"? Uncovering YouTube Server Selection Strategy , 2011, 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN).

[11]  Marco Mellia,et al.  Uncovering the Big Players of the Web , 2012, TMA.

[12]  B. Staehle,et al.  YoMo: A YouTube Application Comfort Monitoring Tool , 2010 .

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

[14]  Phuoc Tran-Gia,et al.  Quantification of YouTube QoE via Crowdsourcing , 2011, 2011 IEEE International Symposium on Multimedia.

[15]  Marco Mellia,et al.  Dissecting Video Server Selection Strategies in the YouTube CDN , 2011, 2011 31st International Conference on Distributed Computing Systems.

[16]  Zhi-Li Zhang,et al.  YouTube traffic dynamics and its interplay with a tier-1 ISP: an ISP perspective , 2010, IMC '10.

[17]  Deep Medhi,et al.  Pytomo: A tool for analyzing playback quality of YouTube videos , 2011, 2011 23rd International Teletraffic Congress (ITC).

[18]  Zhi-Li Zhang,et al.  Vivisecting YouTube: An active measurement study , 2012, 2012 Proceedings IEEE INFOCOM.

[19]  Guillaume Urvoy-Keller,et al.  Challenging statistical classification for operational usage: the ADSL case , 2009, IMC '09.