Spatial Popularity and Similarity of Watching Videos in a Large City

With the popularity of watching mobile videos, many works focus on the geographic features of user viewing behaviors, but few study them in the context of an entire metropolitan city. Different regions of a large city have different intensity of economy activities with respect to their different distances to the downtown, and how this will influence video popularity and similarity is still unclear. To quantitatively study the spatial popularity and similarity of watching videos in a large urban environment, we collect a dataset with two-month video view requests from the largest network provider in Shanghai, containing top six content providers, and study the spatial features of video access in regions of different scales. We find that 1) video popularity and similarity exist at different scales of city division; 2) the concentration of video popularity becomes higher as the region is closer to downtown; 3) when comparing the regions of same scale, the similarity of popular videos becomes lower as the region is farther away from the downtown. Finally, we correlate our findings with cache deployment, advertising and video recommendation to illustrate the implications.

[1]  Cecilia Mascolo,et al.  Track globally, deliver locally: improving content delivery networks by tracking geographic social cascades , 2011, WWW.

[2]  Gaogang Xie,et al.  Watching videos from everywhere: a study of the PPTV mobile VoD system , 2012, IMC '12.

[3]  Gaogang Xie,et al.  On the geographic patterns of a large-scale mobile video-on-demand system , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

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

[5]  Mirjam Wattenhofer,et al.  YouTube around the world: geographic popularity of videos , 2012, WWW.

[6]  Lusheng Ji,et al.  Understanding the impact of network dynamics on mobile video user engagement , 2014, SIGMETRICS '14.

[7]  Gaogang Xie,et al.  User Behavior Characterization of a Large-scale Mobile Live Streaming System , 2015, WWW.

[8]  M. Newman Power laws, Pareto distributions and Zipf's law , 2005 .

[9]  Henrik Abrahamsson,et al.  Program popularity and viewer behaviour in a large TV-on-demand system , 2012, Internet Measurement Conference.

[10]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[11]  Yi Sun,et al.  Mobile video popularity distributions and the potential of peer-assisted video delivery , 2013, IEEE Communications Magazine.

[12]  Pablo Rodriguez,et al.  Watching television over an IP network , 2008, IMC '08.

[13]  Ning Xia,et al.  Inside the bird's nest: measurements of large-scale live VoD from the 2008 olympics , 2009, IMC '09.

[14]  Niklas Carlsson,et al.  The untold story of the clones: content-agnostic factors that impact YouTube video popularity , 2012, KDD.

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

[16]  Ethan Zuckerman,et al.  The International Affiliation Network of YouTube Trends , 2015, ICWSM.

[17]  Ruixi Yuan,et al.  Measurement and analysis of a large scale commercial mobile internet TV system , 2011, IMC '11.

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