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

TL;DR

This paper introduces PIRA, a real-time CDN resource selection algorithm for short video streaming that balances QoE and cost, validated through large-scale experiments showing significant improvements over baseline methods.

Contribution

PIRA is a novel dynamic resource selection algorithm that integrates QoE and cost into a formal model and employs control theoretic methods for efficient, real-time optimization.

Findings

PIRA reduces startup delay by 2.1%.

PIRA shortens rebuffering time by 15.2%.

PIRA lowers average traffic cost by 10%.

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.