Optimal Short Video Ordering and Transmission Scheduling for Reducing Video Delivery Cost in Peer-to-Peer CDNs

TL;DR

This paper introduces an optimal scheduling method for short video delivery in P2P CDNs that reduces costs by intelligently ordering videos to smooth traffic peaks, using a novel polynomial-time algorithm.

Contribution

It formulates the joint video ordering and transmission scheduling as an ILP, reduces it to a MCMF problem, and develops a polynomial-time optimal solution leveraging edge coloring.

Findings

Achieves up to 67% cost reduction over baseline strategies.

Proves the equivalence of scheduling formulations using K"onig's Edge Coloring.

Develops a globally optimal polynomial-time algorithm for the problem.

Abstract

The explosive growth of short video platforms has generated a massive surge in global traffic, imposing heavy financial burdens on content providers. While Peer-to-Peer Content Delivery Networks (PCDNs) offer a cost-effective alternative by leveraging resource-constrained edge nodes, the limited storage and concurrent service capacities of these peers struggle to absorb the intense temporal demand spikes characteristic of short video consumption. In this paper, we propose to minimize transmission costs by exploiting a novel degree of freedom, the inherent flexibility of server-driven playback sequences. We formulate the Optimal Video Ordering and Transmission Scheduling (OVOTS) problem as an Integer Linear Program to jointly optimize personalized video ordering and transmission scheduling. By strategically permuting playlists, our approach proactively smooths temporal traffic peaks, maximizing the offloading of requests to low-cost peer nodes. To solve the OVOTS problem, we provide a rigorous theoretical reduction of the OVOTS problem to an auxiliary Minimum Cost Maximum Flow (MCMF) formulation. Leveraging K\"onig's Edge Coloring Theorem, we prove the strict equivalence of these formulations and develop the Minimum-cost Maximum-flow with Edge Coloring (MMEC) algorithm, a globally optimal, polynomial-time solution. Extensive simulations demonstrate that MMEC significantly outperforms baseline strategies, achieving cost reductions of up to 67% compared to random scheduling and 36% compared to a simulated annealing approach. Our results establish playback sequence flexibility as a robust and highly effective paradigm for cost optimization in PCDN architectures.