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

2026-03-04

AI Summary

This study addresses the challenge of soaring traffic on short-video platforms, which incurs high content delivery costs, while existing peer-to-peer (P2P) CDNs struggle to handle bursty concurrent requests due to limited storage and concurrency at edge nodes. To tackle this, the paper introduces playback order as a novel optimization dimension, jointly optimizing personalized video ranking and transmission scheduling to proactively smooth traffic peaks and maximize offloading of requests to low-cost peer nodes. The authors formulate the problem as OVOTS and rigorously prove its equivalence to the minimum-cost maximum-flow problem. Leveraging König’s edge-coloring theorem, they design MMEC—a globally optimal polynomial-time algorithm. Experiments demonstrate that MMEC reduces transmission costs by up to 67% and 36% compared to random scheduling and simulated annealing, respectively, confirming the substantial benefits of playback sequence optimization in P2P CDNs.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy

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 König'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.

Problem

• short video
• Peer-to-Peer CDN
• video delivery cost
• temporal demand spikes
• edge nodes

Innovation

• Optimal Video Ordering
• Transmission Scheduling
• Peer-to-Peer CDN
• Minimum Cost Maximum Flow
• Edge Coloring