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

Zhipeng Gao, Chunxi Li, Yongxiang Zhao

TL;DR

This paper forms the Optimal Video Ordering and Transmission Scheduling (OVOTS) problem as an Integer Linear Program to jointly optimize personalized video ordering and transmission scheduling, and develops the Minimum-cost Maximum-flow with Edge Coloring (MMEC) algorithm, a globally optimal, polynomial-time solution.

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.

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

TL;DR

This paper forms the Optimal Video Ordering and Transmission Scheduling (OVOTS) problem as an Integer Linear Program to jointly optimize personalized video ordering and transmission scheduling, and develops the Minimum-cost Maximum-flow with Edge Coloring (MMEC) algorithm, a globally optimal, polynomial-time solution.

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.
Paper Structure (29 sections, 1 theorem, 7 equations, 11 figures, 3 tables, 1 algorithm)

This paper contains 29 sections, 1 theorem, 7 equations, 11 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Peer-to-Peer Content Delivery Networks
  4. The Flash-Crowd Pattern of Short Videos
  5. System model and problem formulation
  6. Application scenario
  7. System description
  8. Problem formulation
  9. Theoretical Analysis and Proposed Algorithm
  10. MCMF Problem Construction
  11. Equivalence Analysis
  12. Algorithm Description
  13. Complexity Analysis
  14. Performance Evaluation
  15. Experimental Setup
  16. ...and 14 more sections

Key Result

Theorem 1

Let $OPT_{\mathcal{P}'}$ be the minimum overall delivery cost of the scheduling problem $\mathcal{P}'$, and let $OPT_{\mathcal{F}}$ be the minimum cost of the constructed network flow problem $\mathcal{F}$. Then, $OPT_{\mathcal{P}'} = OPT_{\mathcal{F}}$.

Figures (11)

  • Figure 1: Jaccard similarity of the top 0.1% most requested videos across 2-hour time slots for short video and traditional VoD datasets, illustrating the flash-crowd request pattern and rapid popularity decay of short videos compared to traditional VoD.
  • Figure 2: Jaccard similarity of the top 0.1% most requested videos across fine-grained 10-minute time slots during the evening peak period (21:30–23:20). The similarity drops below 0.5 within just 30 minutes, demonstrating the short video flash crowds.
  • Figure 3: System architecture and motivating example of the proposed joint optimization framework.
  • Figure 4: An example of capacity collisions under identical chronological sequences.
  • Figure 5: Auxiliary flow network representing the aggregated video delivery requests and virtual node capacities.
  • ...and 6 more figures

Theorems & Definitions (2)

  • Theorem 1
  • proof