Understanding Watching Patterns of Live TV Programs on Mobile Devices: A Content Centric Perspective = Understanding Watching Patterns of Live TV Programs on Mobile Devices: A Content Centric Perspective

다국어 초록 (Multilingual Abstract)

With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.

참고문헌 (Reference)

1 P. Gill, "Youtube traffic characterization: a view from the edge" ACM 8-15, 2007

2 B. Stelter, "Youths are watching, but less often on TV" The New York Times

3 Z. Liu, "Why Are Peers Less Stable in Unpopular P2P Streaming Channels?" Springer Berlin Heidelberg 274-286, 2009

4 Z. Li, "Watching Videos from Everywhere: A Study of the PPTV Mobile VoD System" ACM 185-198, 2012

5 M. Cha, "Watching Television over an IP Network" ACM 71-84, 2008

6 P. Danaher, "Using a nested logit model to forecast television ratings" 28 : 607-622, 2012

7 E. Kaasinen, "User Acceptance of Mobile TV Services" ACM 34:1-34:10, 2009

8 H. Yu, "Understanding user behavior in large-scale video-on-demand systems" 40 : 333-344, 2006

9 Junghwan Lee, "SVC-based Adaptive Video Streaming over Content-Centric Networking" 한국인터넷정보학회 7 (7): 2430-2447, 2013

10 Y. Jin, "Reducing Operational Costs in Cloud Social TV: An Opportunity for Cloud Cloning" 16 : 1739-1751, 2014

11 D. Niu, "Quality-assured cloud bandwidth auto-scaling for video-on-demand applications" 460-468, 2012

12 I. Ketykó, "QoE Measurement of Mobile YouTube Video Streaming" ACM 27-32, 2010

13 C. Wu, "On Dynamic Server Provisioning in Multichannel P2P Live Streaming" 19 : 1317-1330, 2011

14 W. Tang, "Modeling and generating realistic streaming media server workloads" 51 : 336-356, 2007

15 T. Qiu, "Modeling User Activities in a Large IPTV System" ACM 430-441, 2009

16 Wen Hui, "MediaCloud: A New Paradigm of Multimedia Computing" 한국인터넷정보학회 6 (6): 1153-1170, 2012

17 Y. Chen, "Measurement and Modeling of Video Watching Time in a Large-Scale Internet Video-on-Demand System" 15 : 2087-2098, 2013

18 Y. Li, "Measurement and Analysis of a Large Scale Commercial Mobile Internet TV System" ACM 209-224, 2011

19 K.-W. Hwang, "Leveraging Video Viewing Patterns for Optimal Content Placement" Springer 44-58, 2012

20 M. Cha, "I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system" ACM 1-4, 2007

21 P. J. Danaher, "Forecasting television ratings" 27 : 1215-1240, 2011

22 Y. Xiao, "Energy Consumption of Mobile YouTube: Quantitative Measurement and Analysis" 61-69, 2008

23 N. Liu, "Dissecting User Behaviors for a Simultaneous Live and VoD IPTV System" 10 : 23:1-23:16, 2014

24 C. Wu, "Diagnosing Network-wide P2P Live Streaming Inefficiencies" 8 : 13:1-13:19, 2012

25 D. Niu, "Demand forecast and performance prediction in peer-assisted on-demand streaming systems" 421-425, 2011

26 Y. Wen, "Cloud Mobile Media: Reflections and Outlook" 16 : 885-902, 2014

27 Cisco, "Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018" 2014

28 G. Yu, "Analysis and characterization of IPTV user behavior" 1-6, 2009

29 K. Sripanidkulchai, "An Analysis of Live Streaming Workloads on the Internet" ACM 41-54, 2004

30 Amazon, "Amazon CloudFront Pricing"

31 Alibaba Group, "Ali Cloud CDN Pricing (in Chinese)"

32 Y. Liu, "A server’s perspective of Internet streaming delivery to mobile devices" 1332-1340, 2012

33 E. Veloso, "A hierarchical characterization of a live streaming media workload" 14 : 133-146, 2006

34 Y. -Y. Huang, "A Weight-Sharing Gaussian Process Model Using Web-Based Information for Audience Rating Prediction" Springer International Publishing 198-208, 2014

35 Y. -H. Cheng, "A Predicting Model of TV Audience Rating Based on the Facebook" 1034-1037, 2013

36 X. Hei, "A Measurement Study of a Large-Scale P2P IPTV System" 9 : 1672-1687, 2007