Competitive Analysis of Data Sponsoring and Edge Caching for Mobile Video Streaming

Cellular data sponsoring (CDS) is a traditional data sponsor scheme widely used in cellular video delivery networks, where content providers (CPs) bear the cellular data downloading cost for mobile video users (MUs), so as to attract more MUs and achieve higher revenue (e.g., via more attached advertisements). Edge caching sponsoring (ECS) is a novel data sponsor scheme recently introduced in the emerging 5G network, where CPs cache popular video contents on the edge network in advance and deliver them to local MUs directly. Thus, it can not only achieve the benefits of CDS (i.e., attracting more MUs and achieving higher revenue), but also reduce the congestion of backhaul network. In this work, we will perform a competitive analysis of CDS and ECS for mobile video streaming. Specifically, we consider a mobile video delivery network with two CPs who adopt CDS and ECS, respectively. MUs can choose one or neither of these two sponsor schemes (from the corresponding CPs) for his video content requests. We formulate the interaction of CPs and MUs as a two-stage Stackelberg game, where CPs act as leaders determining the efforts of their adopted sponsor schemes in the first stage, and MUs act as followers choosing the best sponsor schemes for their content requests in the second stage. We analyze the sub-game perfect equilibrium systematically for both cooperative and competitive scenarios (depending on whether two CPs cooperate or compete with each other). Numerical results show that in the competitive scenario, the joint sponsor of ECS and CDS can increase the total MU payoff by 36% ~ 140%, comparing with that with only one sponsor scheme. Moreover, the CPs can benefit more from ECS than from CDS when the revenue is higher.

[1]  Alexandros G. Dimakis,et al.  FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers , 2013, IEEE Transactions on Information Theory.

[2]  Sangtae Ha,et al.  Sponsoring mobile data: An economic analysis of the impact on users and content providers , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[3]  Chong-Wah Ngo,et al.  Practical elimination of near-duplicates from web video search , 2007, ACM Multimedia.

[4]  Bruce Bueno de Mesquita,et al.  An Introduction to Game Theory , 2014 .

[5]  Yao Wang,et al.  Video coding using a self-adaptive redundant dictionary consisting of spatial and temporal prediction candidates , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[6]  Haitian Pang,et al.  Joint Sponsor Scheduling in Cellular and Edge Caching Networks for Mobile Video Delivery , 2018, IEEE Transactions on Multimedia.

[7]  Ming Tang,et al.  A General Framework for Crowdsourcing Mobile Communication, Computation, and Caching , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[8]  Dong-Qing Zhang,et al.  Assessing quality of experience for adaptive HTTP video streaming , 2014, 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[9]  Yue Jin,et al.  Pricing sponsored content in wireless networks with multiple content providers , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[10]  Haitian Pang,et al.  Joint Optimization of Data Sponsoring and Edge Caching for Mobile Video Delivery , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[11]  Haitian Pang,et al.  When Data Sponsoring Meets Edge Caching: A Game-Theoretic Analysis , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[12]  Mehdi Bennis,et al.  Living on the edge: The role of proactive caching in 5G wireless networks , 2014, IEEE Communications Magazine.

[13]  Dilip Kumar Krishnappa,et al.  On the Feasibility of Prefetching and Caching for Online TV Services: A Measurement Study on Hulu , 2011, PAM.

[14]  Richard T. B. Ma,et al.  Thunder crystal: a novel crowdsourcing-based content distribution platform , 2015, NOSSDAV '15.