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.

翻译：在大规模短视频平台中，CDN资源选择对于维持用户体验质量（QoE）同时控制不断增长的流量成本至关重要。为深入理解这一现象，我们在生产级短视频系统中对视频播放期间的网络状况进行了实际测量。结果表明，提供更高平均QoE的CDN往往伴随着更高的经济成本，且其连接质量甚至在单个视频播放期间也会波动，这揭示了QoE与成本之间存在根本性的动态权衡。然而，在短视频流媒体的CDN选择场景中，如何在成本约束下维持高QoE的问题仍未得到充分研究。为此，我们提出PIRA——一种在视频播放期间实时优化QoE与成本的动态资源选择算法。PIRA通过数学模型将QoE与成本进行形式化整合，并引入基于控制理论的视频内CDN资源选择方法，能够在网络动态变化下平衡QoE与成本。为降低计算开销，PIRA采用状态空间剪枝与自适应参数调整技术，高效求解高维优化问题。在为期两周、覆盖45万用户的大规模生产实验中，PIRA显著优于现有生产基线：启动延迟降低2.1%，卡顿时间缩短15.2%，平均单位流量成本下降10%，充分证明了其在规模化场景中平衡用户体验与经济成本的有效性。