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A Game-theoretic analysis on the economic viability of mobile content pre-staging

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Abstract

The rapid growth of demand for data in wireless communications has driven the mobile service carriers and the research community to seek both effective technical and alternative solutions to the data demand problem. One particular solution, content pre-staging, tries to push content as close to the mobile device as possible in order to lower demand at peak times. Assuming the interesting case that mobile device storage could be made available as part of the mobile carrier’s system capacity either directly by the end user or indirectly by the carrier, this paper investigates the potential economic impacts on the mobile service business and various stakeholders of content pre-staging. We explore the economic implications of content pre-staging by modeling the interplay among the mobile carrier, end users, and the content provider in a game theoretic framework. The carrier designs pricing mechanisms to affect the behaviors of the content provider and end users for the purpose of profit maximization. In particular, two prices are introduced, the price charged to the content provider to pre-stage content on mobile device storage, and the monetary reward to compensate users for the usage of their mobile device storage. Although the individual incentive of the carrier is not necessarily aligned with social incentives, the welfare analysis of content pre-staging shows that the practice improves social welfare by increasing network efficiency. Localizing content increases the overall profitability of mobile service business which is positively related to the relevance of the pre-staged content. The carrier’s pricing mechanisms determine the manner in which the increased profitability of the business is shared by various interested parties. While the carrier may design prices strategically to retain a larger share of the increased profitability, content pre-staging can benefit all the three parties in the game, i.e., the carrier gains in saved capacity and new revenue, users gain QoE, content, and financial rewards for sharing mobile device storage, and the content provider gains in increased revenue from increased content access.

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References

  1. RajanishEmail, N., & Banakar, R. M. (2019). Cognitive radio: A conceptual future radio and spectrum sensing techniques—A survey. In S. Satapathy, K. Raju, K. Shyamala, D. Krishna, & M. Favorskaya (Eds.), Advances in decision sciences, image processing, security and computer vision (Vol. 4, pp. 155–165)., Learning and analytics in intelligent systems book series (LAIS) Cham: Springer.

    Chapter  Google Scholar 

  2. Han, B., Hui, P., Kumar, V. A., Marathe, M. V., Pei, G., & Srinivasan, A. (2010). Cellular traffic offloading through opportunistic communications: A case study. In Proceedings of the 5th ACM workshop on challenged networks, Chicago, Illinois, September 24 (pp. 31–38).

  3. Zhuo, X., Gao, W., Cao, G., & Hua, S. (2014). An incentive framework for cellular traffic offloading. IEEE Transactions on Mobile Computing, 13(3), 541–555.

    Article  Google Scholar 

  4. Chen, X., Wu, J., Cai, Y., Zhang, H., & Chen, T. (2015). Energy-efficiency oriented traffic offloading in wireless networks: A brief survey and a learning approach for heterogeneous cellular networks. IEEE Journal on Selected Areas in Communications, 33(4), 627–640.

    Article  Google Scholar 

  5. Zhou, H., Wang, H., Li, X., & Leung, V. C. M. (2018). A survey on mobile data offloading technologies. IEEE Access, 6, 5101–5111.

    Article  Google Scholar 

  6. Paschos, G. S., Iosifidis, G., Tao, M., Towsley, D., & Caire, G. (2018). The role of caching in future communication systems and networks. IEEE Journal on Selected Areas in Communications, 36(6), 1111–1125.

    Article  Google Scholar 

  7. Ji, M., Caire, G., & Molisch, A. F. (2015). Wireless device-to-device caching networks: Basic principles and system performance. IEEE Journal on Selected Areas in Communications, PP(99), 1.

    Google Scholar 

  8. Finamore, A., Mellia, M., Gilani, Z., Papagiannaki, K., Erramilli, V., & Grunenberger, Y. (2013). Is there a case for mobile phone content pre-staging? In Proceedings of the 9th ACM conference on emerging networking experiments and technologies (CoNEXT’13), New York, NY (pp. 321–326).

  9. Striegel, A., Hu, X., & Song, L. (2015). A case for making mobile device storage accessible by an operator. IEEE COMSOC MMTC E-Letter, 10(1), 7–10.

    Google Scholar 

  10. Sen, S., Joe-Wong, C., Ha, S., & Chiang, M. (2013). A survey of smart data pricing: Past proposals, current plans, and future trends. ACM Computing Surveys (CSUR), 46(2), 1–37.

    Article  Google Scholar 

  11. Aisamuddin, A., Shariff, M., Katuk, N., & Zakaria, N. H. (2017). An overview to pre-fetching techniques for content caching of mobile applications. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2–12), 37–43.

    Google Scholar 

  12. Cao, P. (2015). Opportunities and challenges for caching and prefetching on mobile devices. In Proceedings of 3rd IEEE workshop on hot topics in web systems and technologies (HotWeb), Washington, DC, November 12–13.

  13. Bao, X., Gowda, M., Mahajan, R., & Choudhury, R. R. (2013). The case for psychological computing. In Proceedings of the 14th workshop on mobile computing systems and applications (HotMobile’13), no. Article No. 6, Jekyll Island, Georgia, February 26–27.

  14. Ding, N., Wagner, D., Chen, X., Pathak, A., Hu, Y. C. & Rice, A. (2013). Characterizing and modeling the impact of wireless signal strength on smartphone battery drain. In Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems, Pittsburgh, PA, June 17–21, 2013 (pp. 29–40).

  15. Li, Y., Wang, Y., & Lan, T. (2018). Mobile ad prefetching and energy optimization via tail energy accounting. IEEE Transactions on Mobile Computing, 18(9), 2117–2128.

    Article  Google Scholar 

  16. Qian, F., Huang, J., Erman, J., Mao, Z. M., Sen, S. & Spatscheck, O. (2013). How to reduce smartphone traffic volume by 30%? In Proceedings of the 14th international conference on passive and active measurement (PAM), Hong Kong, China, March 18–19, 2013 (pp. 42–52).

  17. Zhang, L., Wu, W., & Wang, D. (2014). Time dependent pricing in wireless data networks: Flat-rate vs. usage-based schemes. In Proceedings of IEEE INFOCOM, Toronto, Canada, April 27–May 2, 2014.

  18. Ha, S., Sen, S., Joe-Wong, C., Im, Y., & Chiang, M. (2012). Tube: Time-dependent pricing for mobile data. In Proceedings of the ACM SIGCOMM conference on applications, technologies, architectures, and protocols for computer communication, Helsinki, Finland, August 13–17, 2012 (pp. 247–258).

  19. Joe-Wong, C., Ha, S., Sen, S., & Chiang, M. (2015). Do mobile data plans affect usage? Results from a pricing trial with ISP customers. In Proceedings of the 16th international conference on passive and active network measurement (PAM), New York, NY, March 19–20, 2015 (pp. 96–108).

  20. Andrews, M., Ozen, U., Reiman, M. I., & Wang, Q. (2013). Economic models of sponsored content in wireless networks with uncertain demand. In Proceedings of IEEE INFOCOM workshop on smart data pricing (SDP), Turin, Italy, April 14–19, 2013 (pp. 345–350).

  21. Joe-Wong, C., Ha, S., & Chiang, M. (2015). Sponsoring mobile data: An economic analysis of the impact on users and content providers. In Proceedings of IEEE INFOCOM, Kowloon, Hong Kong, April 26–May 1, 2015.

  22. Joe-Wong, C., Sen, S., & Ha, S. (2018). Sponsoring mobile data: Analyzing the impact on internet stakeholders. IEEE/ACM Transactions on Networking, 26(3), 1179–1192.

    Article  Google Scholar 

  23. Zhang, L., Wu, W., & Wang, D. (2015). Sponsored data plan: A two-class service model in wireless data networks. In Proceedings of the ACM SIGMETRICS international conference on measurement and modeling of computer systems, Portland, OR, June 15–19, 2015 (pp. 85–96).

  24. Hande, P., Chiang, M., Calderbank, R., & Rangan, S. (2009). Network pricing and rate allocation with content provider participation. In Proceedings of IEEE INFOCOM, Rio de Janeiro, Brazil, April 19–25, 2009 (pp. 990–998).

  25. Andrews, M., Bruns, G., & Lee, H. (2014). Calculating the benefits of sponsored data for an individual content provider. In Proceedings of the 48th annual conference on information sciences and systems (CISS), Princeton, NJ, March 19–21, 2014 (pp. 1–6).

  26. Zhang, L., & Wang, D. (2014). Sponsoring content: Motivation and pitfalls for content service providers. In Proceedings of IEEE conference on computer communications workshops (INFOCOM WKSHPS), Toronto, Canada, April 27–May 2, 2014 (pp. 577–582).

  27. Thilakarathna, K., Jiang, F.-Z., Mrabet, S., Kaafar, M. A., Seneviratne, A., & Xie, G. (2017). Crowd-cache: Leveraging on spatio-temporal correlation in content popularity for mobile networking in proximity. Computer Communications, 100, 104–117.

    Article  Google Scholar 

  28. Lau, C. P., Alabbasi, A., & Shihada, B. (2018). An efficient content delivery system for 5G CRAN employing realistic human mobility. IEEE Transactions on Mobile Computing, 18(4), 742–756.

    Article  Google Scholar 

  29. Asadi, A., Wang, Q., & Mancuso, V. (2014). A survey on device-to-device communication in cellular networks. IEEE Communications Surveys & Tutorials, 16(4), 1801–1819.

    Article  Google Scholar 

  30. Malak, D., Al-Shalash, M., & Andrews, J. G. (2016). Optimizing content caching to maximize the density of successful receptions in device-to-device networking. IEEE Transactions on Communications, 64(10), 4365–4380.

    Google Scholar 

  31. Wu, Y., Yao, S., Yang, Y., Zhou, T., Qian, H., Hu, H., et al. (2016). Challenges of mobile social device caching. IEEE Access, 4, 8938–8947.

    Article  Google Scholar 

  32. Chen, Z., Liu, Y., Zhou, B., & Tao, M. (2016). Caching incentive design in wireless d2d networks: A stackelberg game approach. In IEEE international conference on communications (ICC), Kuala Lumpur, Malaysia, May 23–27, 2016.

  33. Li, Z., Liao, Q., & Striegel, A. D. (2016). On the economics of mobile content pre-staging. In The 5th IEEE INFOCOM workshop on smart data pricing (SDP), San Francisco, CA, April 11, 2016.

  34. Tadrous, J., Eryilmaz, A., & Gamal, H. E. (2014). Joint pricing and proactive caching for data services: Global and user-centric approaches. In Proceedings of IEEE INFOCOM workshop on smart data pricing (SDP), Toronto, ON, April 27–May 2, 2014 (pp. 616–621).

  35. Ma, R. T. B., & Towsley, D. (2015). Cashing in on caching: On-demand contract design with linear pricing. In Proceedings of the 11th ACM conference on emerging networking experiments and technologies (CoNEXT), Heidelberg, Germany, December 1–4, 2015.

  36. Alotaibi, F., Hosny, S., Tadrous, J., Gamal, H. E., & Eryilmaz, A. (2015). Towards a marketplace for mobile content: Dynamic pricing and proactive caching. arXiv preprint arXiv: 1511.07573.

  37. Arrow, K. J. (1971). The theory of risk aversion. In K. Arrow (Ed.), Essays in the theory of risk-bearing (pp. 90–120). Chicago: Markham.

    Google Scholar 

  38. Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica, 32(1–2), 122–136.

    Article  Google Scholar 

  39. Lyons, D. (2015). Google wireless and the evolution of usage-based pricing. TechPolicyDaily.com, May 6, 2015. Available: http://www.techpolicydaily.com/internet/google-usage-based-pricing/

  40. Agarwal, S. (2006). Intelligent content caching for mobile devices. In Proceedings of the 13th international conference on telecommunications, May 9–12, 2006 (p. 4).

  41. Quan, W., Liu, Y., Jiang, X., & Guan, J. (2016). Intelligent popularity-aware content caching and retrieving in highway vehicular networks. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-016-0688-z.

    Article  Google Scholar 

  42. PWC, IAB internet advertising revenue report: 2018 first six months results. PricewaterhouseCoopers, November 2018.

  43. Louis, T. (2013). The real price of wireless data, Forbes, September 22, 2013.

  44. Leubsdorf, B. (2017). How cell-phone plans with unlimited data limited inflation. The Wall Street Journal, May 19, 2017.

  45. DeGrasse, M. (2017). How carriers are controlling network opex, RCR Wireless News, September 22, 2017.

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Correspondence to Qi Liao.

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Li, Z., Liao, Q. & Striegel, A.D. A Game-theoretic analysis on the economic viability of mobile content pre-staging. Wireless Netw 26, 667–683 (2020). https://doi.org/10.1007/s11276-019-02176-3

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