Revenue Optimization in Wireless Video Caching Networks: A Privacy-Preserving Two-Stage Solution

TL;DR

This paper presents a privacy-preserving two-stage approach using federated learning and demand prediction to optimize cache placement and maximize revenue in wireless video caching networks.

Contribution

It introduces a novel two-stage, privacy-preserving framework combining federated learning and demand estimation for revenue optimization in wireless caching.

Findings

Federated learning achieves near-centralized demand prediction performance.

The proposed method outperforms existing caching strategies.

Revenue optimization offers deeper insights than traditional cache hit ratio methods.

Abstract

Video caching can significantly improve delivery efficiency and enhance quality of video streaming, which constitutes the majority of wireless communication traffic. Due to limited cache size, caching strategies must be designed to adapt to and dynamic user demand in order to maximize system revenue. The system revenue depends on the benefits of delivering the requested videos and costs for (a) transporting the files to the users and (b) cache replacement. Since the cache content at any point in time impacts the replacement costs in the future, demand predictions over multiple cache placement slots become an important prerequisite for efficient cache planning. Motivated by this, we introduce a novel two-stage privacy-preserving solution for revenue optimization in wireless video caching networks. First, we train a Transformer using privacy-preserving federated learning (FL) to predict multi-slot future demands. Given that prediction results are never entirely accurate, especially for longer horizons, we further combine global content popularity with per-user prediction results to estimate the content demand distribution. Then, in the second stage, we leverage these estimation results to find caching strategies that maximize the long-term system revenue. This latter problem takes on the form of a multi-stage knapsack problem, which we then transform to a integer linear program. Our extensive simulation results demonstrate that (i) our FL solution delivers nearly identical performance to that of the ideal centralized solution and outperforms other existing caching methods, and (ii) our novel revenue optimization approach provides deeper system performance insights than traditional cache hit ratio (CHR)-based optimization approaches.