Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2019-04-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
9
1864-1872
Series
INFOCOM 2019 - IEEE Conference on Computer Communications, Proceedings - IEEE INFOCOM, Volume 2019-April
Abstract
Content providers (CPs) are keen to cache their popular contents in small-cell base stations (SBSs) provided by mobile network operators (MNOs). In fact, they can serve the requests of their subscribers with low latency, thereby increasing user satisfaction. Employing advanced video encoding techniques, such as scalable video coding (SVC), improves the utilization of wireless resources and the network infrastructure. However, the cache trading policies for SVC videos in multi-provider networks have not been studied yet. In this article, we design a commercial trading system in which multiple CPs, each owning SVC videos, compete over renting the cache in multiple SBSs provided by an MNO. We model cache trading between the MNO and CPs as a social welfare maximization problem, whose objective is to maximize the trading profit while achieving the economic properties of rationality, balanced budget, and truthfulness. Since optimal allocation of random-size caches to multiple CPs is NP-hard, we devise an iterative trading mechanism based on double auction called DOCAT, wherein the cache of SBSs is segmented and traded in multiple rounds. In each round of the auction, the MNO and CPs price the cache segments based on their profit, then submit their asking and buying bids, respectively. Next, a many-to-one matching algorithm is run to efficiently find perfect matches between the cache segments and winning CPs. Numerical results based on a real video dataset show that DOCAT increases the social welfare of the system while satisfying the desired economic properties.
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Keywords
auction, edge caching, game theory, Heterogeneous networks, network economics, scalable video coding
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Citation
Jedari, B & Di Francesco, M 2019, Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks . in INFOCOM 2019 - IEEE Conference on Computer Communications ., 8737433, Proceedings - IEEE INFOCOM, vol. 2019-April, IEEE, pp. 1864-1872, IEEE Conference on Computer Communications, Paris, France, 29/04/2019 . https://doi.org/10.1109/INFOCOM.2019.8737433