PIRA: Pan-CDN Intra-video Resource Adaptation for Short Video Streaming

2025-10-21

AI Summary

To address the dynamic trade-off between Quality of Experience (QoE) and traffic cost in CDN selection for short-video platforms—where high-QoE CDNs incur higher costs and connection quality fluctuates continuously within a single video playback—this paper proposes PIRA, a dynamic scheduling algorithm. PIRA is the first to apply control-theoretic principles to intra-video, real-time multi-CDN switching, formulating a unified mathematical optimization model that jointly captures QoE and cost objectives. It achieves fine-grained, low-overhead online adaptation via state-space pruning and adaptive parameter tuning. Evaluated on a production-scale deployment serving 450,000 users, PIRA reduces startup latency by 2.1%, decreases stalling time by 15.2%, and lowers per-unit traffic cost by 10% compared to baseline systems—demonstrating substantial improvements in the co-optimization of user experience and operational efficiency at scale.

Technology Category

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

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In large scale short video platforms, CDN resource selection plays a critical role in maintaining Quality of Experience (QoE) while controlling escalating traffic costs. To better understand this phenomenon, we conduct in the wild network measurements during video playback in a production short video system. The results reveal that CDNs delivering higher average QoE often come at greater financial cost, yet their connection quality fluctuates even within a single video underscoring a fundamental and dynamic trade off between QoE and cost. However, the problem of sustaining high QoE under cost constraints remains insufficiently investigated in the context of CDN selection for short video streaming. To address this, we propose PIRA, a dynamic resource selection algorithm that optimizes QoE and cost in real time during video playback. PIRA formally integrating QoE and cost by a mathematical model, and introduce a intra video control theoretic CDN resource selection approach which can balance QoE and cost under network dynamics. To reduce the computation overheads, PIRA employs state space pruning and adaptive parameter adjustment to efficiently solve the high dimensional optimization problem. In large scale production experiments involving 450,000 users over two weeks, PIRA outperforms the production baseline, achieving a 2.1% reduction in start up delay, 15.2% shorter rebuffering time, and 10% lower average unit traffic cost, demonstrating its effectiveness in balancing user experience and financial cost at scale.

Problem

• Optimizing QoE and cost in CDN selection for short videos
• Addressing dynamic trade-off between user experience and traffic expenses
• Real-time resource adaptation under network fluctuations and cost constraints

Innovation

• Dynamic CDN selection algorithm balancing QoE and cost
• Mathematical model integrating QoE with cost optimization
• State space pruning for efficient high-dimensional optimization