Mobile Edge Caching: An Optimal Auction Approach

TL;DR

This paper introduces an auction-based mechanism for mobile edge caching that maximizes service provider revenue by efficiently allocating cache space and content delivery quality, while incentivizing truthful user valuation reporting.

Contribution

It proposes an optimal auction mechanism for cache allocation and delivery quality that accounts for user valuation privacy and maximizes SP revenue, with a computationally efficient solution.

Findings

Designed an incentive-compatible auction mechanism

Derived a closed-form solution for optimal delivery quality

Ensured computational efficiency in cache allocation

Abstract

With the explosive growth of wireless data, the sheer size of the mobile traffic is challenging the capacity of current wireless systems. To tackle this challenge, mobile edge caching has emerged as a promising paradigm recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality.