A pricing scheme for content caching in 5G mobile edge clouds

The endeavor to develop 5G technology aims to support the recent outstanding mobile data traffic growth. In this regard, mobile network providers will be able to leverage on cloud edge-caching to offer services with enhanced quality of experience on the move. By this technology, dedicated cache space of mobile networks can be provisioned to OTT content providers, e.g., over metropolitan areas covered the network of a mobile network provider. In this work we address the problem of fair pricing such caching service, with storage the actual shared resource for caching. We study a scheme in which contents are dynamically stored in the edge memory. The mobile network provider offers a price λ for storing contents on the shared cache, thus engendering competition for cache memory sharing among content providers. We model such competition among OTT content providers using the economic notion of Kelly mechanism. Hence, we have studied the Stackelberg equilibrium, i.e., the optimal price configuration for the network provider. Numerical results describe the structure of the Nash equilibrium and the optimal prices resulting from the network provider optimal strategy.

[1]  Giuseppe Caire,et al.  Fundamental limits of distributed caching in D2D wireless networks , 2013, 2013 IEEE Information Theory Workshop (ITW).

[2]  Ming Xiao,et al.  Efficient Video Pricing and Caching in Heterogeneous Networks , 2016, IEEE Transactions on Vehicular Technology.

[3]  Giuseppe Caire,et al.  Wireless caching: technical misconceptions and business barriers , 2016, IEEE Communications Magazine.

[4]  Donald F. Towsley,et al.  On the complexity of optimal routing and content caching in heterogeneous networks , 2014, 2015 IEEE Conference on Computer Communications (INFOCOM).

[5]  Ramesh Johari,et al.  Efficiency loss in market mechanisms for resource allocation , 2004 .

[6]  Tamer Basar,et al.  Efficient signal proportional allocation (ESPA) mechanisms: decentralized social welfare maximization for divisible resources , 2006, IEEE Journal on Selected Areas in Communications.

[7]  H. Vincent Poor,et al.  A Learning-Based Approach to Caching in Heterogenous Small Cell Networks , 2015, IEEE Transactions on Communications.

[8]  T. Başar,et al.  Dynamic Noncooperative Game Theory, 2nd Edition , 1998 .

[9]  Eitan Altman,et al.  Game theory approach for modeling competition over visibility on social networks , 2014, 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS).

[10]  Suhas N. Diggavi,et al.  Multi-level coded caching , 2014, 2014 IEEE International Symposium on Information Theory.

[11]  R. Michael Buehrer,et al.  Learning distributed caching strategies in small cell networks , 2014, 2014 11th International Symposium on Wireless Communications Systems (ISWCS).

[12]  Richard T. B. Ma,et al.  Price differentiation and control in the Kelly mechanism , 2013, Perform. Evaluation.

[13]  Xiaofei Wang,et al.  Cache in the air: exploiting content caching and delivery techniques for 5G systems , 2014, IEEE Communications Magazine.

[14]  J. Goodman Note on Existence and Uniqueness of Equilibrium Points for Concave N-Person Games , 1965 .

[15]  Chung Gu Kang,et al.  Mobile device-to-device (D2D) content delivery networking: A design and optimization framework , 2014, Journal of Communications and Networks.

[16]  Walid Saad,et al.  Cache-aware user association in backhaul-constrained small cell networks , 2014, 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).