User Behavior towards Video Content in Mobile Devices for Designing Individualistic Prefetching Algorithm

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Prefetching content in mobile network environment are meant to shorten users perceive waiting time. Different approaches have been taken by various literatures to provide an algorithm that is able to predict user request ahead of time. A questionnaire was design and distributed to verify approaches taken by those research using new respondents. Base on the response analyzed it is found that the most important aspect that influenced user behavior towards video content is the relevancy of the topics and WiFi is the most preferred type of connections to be used to download video content. Another behavior observed is that users who are willing to watch prefetched video also tends to download a new video. The analysis also confirms that user could be categorized to heavy and light users and they behave differently during weekdays and weekends. Findings from this survey would hopefully become guidance in designing a prefetching algorithm that suited individual needs.

     


  • Keywords


    Mobile Communication; Prefetching; Survey; Video Content

  • References


      [1] Cisco Mobile, “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021 White Paper,” 2017.

      [2] J. Liao, F. Trahay, G. Xiao, L. Li, and Y. Ishikawa, “Performing Initiative Data Prefetching in Distributed File Systems for Cloud Computing,” IEEE Trans. Cloud Comput., vol. 5, no. 3, pp. 550–562, 2017.

      [3] A. Trifonova and M. Ronchetti, “Hoarding Content in M-Learning Context.”

      [4] I. Kilanioti and G. A. Papadopoulos, “Content delivery simulations supported by social network-awareness,” Simul. Model. Pract. Theory, vol. 76, pp. 47–66, 2017.

      [5] C. Wu, X. Chen, Y. Zhou, N. Li, X. Fu, and Y. Zhang, “Spice : Socially-Driven Learning-Based Mobile Media Prefetching,” in International Conference on Computer Communications, 2016.

      [6] T. Paul, D. Puscher, S. Wilk, and T. Strufe, “Systematic, large-scale analysis on the feasibility of media prefetching in Online Social Networks,” in 2015 12th Annual IEEE Consumer Communications and Networking Conference, CCNC 2015, 2015, pp. 755–760.

      [7] H. Li, J. Zhang, H. Lv, and Q. Lin, “Web prefetching of smart wireless access point,” in Proceedings - 2016 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2016, 2016, pp. 211–216.

      [8] J. Flinn, T. J. Giuli, B. Noble, C. Peplin, and D. Watson, “Informed mobile prefetching,” in Proceedings of the 10th international conference on Mobile systems, applications, and services - MobiSys ’12, 2012, p. 155.

      [9] O. K. Shoukry and M. M. Fayek, “Evolutionary Scheduler for Content Pre-Fetching in Mobile Networks,” in International Conference on Machine Learning and Applications., 2011, pp. 114–119.

      [10] Y. Li, Y. Zhang, and R. Yuan, “Measurement and analysis of a large scale commercial mobile internet TV system,” in Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference - IMC ’11, 2011, p. 209.

      [11] J. Han, X. Y. Li, T. Jung, J. Zhao, and Z. Zhao, “Network agile preference-based prefetching for mobile devices,” in 2014 IEEE 33rd International Performance Computing and Communications Conference, IPCCC 2014, 2015, no. 61170216.

      [12] T. Hoßfeld, R. Schatz, E. Biersack, and L. Plissonneau, “Internet Video Delivery in YouTube: From Traffic Measurements to Wuality of Experience,” Data Traffic Monit. Anal. From Meas. Classif. Anom. Detect. to Qual. Exp., no. Part III, pp. 266–303, 2013.

      [13] T. Murata and K. Saito, “Extracting users’ interests from Web log data,” in Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), WI’06, 2007, pp. 343–346.

      [14] A. Gouta, D. Hausheer, A. M. Kermarrec, C. Koch, Y. Lelouedec, and J. Rückert, “CPSys: A System for Mobile Video Prefetching,” in Proceedings - IEEE Computer Society’s Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS, 2015, vol. 2015–Novem, pp. 188–197.

      [15] X. Bao, M. Gowda, R. Mahajan, and R. R. Choudhury, “The case for psychological computing,” in Proceedings of the 14th Workshop on Mobile Computing Systems and Applications - HotMobile ’13, 2013, p. 1.

      [16] Y. Li, W. M. Gifford, and A. Sheopur, “A Case Study of Mobile User Behaviors Using Spatio-temporal Data,” in 2016 17th IEEE International Conference on Mobile Data Management (MDM), 2016, pp. 298–301.

      [17] R. Rashkovits, “Preference-Based Long-Term Prefetcing Using Latency-Obsolescence Tradeoff,” in 2016 International Conference on High Performance Computing & Simulation (HPCS), 2016, pp. 357–363.

      [18] “Survey Monkey.” [Online]. Available: https://www.surveymonkey.com/.


 

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Article ID: 23726
 
DOI: 10.14419/ijet.v7i4.31.23726




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