Caching and Recommendation Decisions at Transcoding-Enabled Base Stations

Abstract

In the context of on-demand video streaming services, both the caching and the recommendation decisions have an impact on the user satisfaction, and thus, financial implications for the Content Provider (CP). The idea of co-designing these decisions has been recently proposed in the literature as a way to minimize delivery costs and traffic at the backbone Internet. However, related work does not take into account that every content exists in multiple versions/streaming qualities, or at best treats each version as a separate content, when it comes to caching. In this paper, we explore how transcoding a content at the edge could avoid placing multiple related versions of this content in the same cache, thus better utilizing capacity (leading to an increase of the CP's profit). To this end, we formulate the problem of jointly deciding on caching, recommendations, and user-transcoder assignments with the goal of increasing the profit (revenue minus the incurred costs). We propose an iterative algorithm that is based on a decomposition of the formulated problem into two subproblems. We show that both subproblems, although NP-hard, are equivalent to problems in the literature for which algorithms with approximation guarantees exist. Our numerical evaluations in realistic scenarios show that the proposed policy leads to important financial gains of up to 29% when compared to the scenario where edge transcoding is not exploited.