Optimizing quality of experience (QoE) for live video streaming (LVS) remains a long-standing challenge for content delivery network (CDN) providers. Today’s CDNs predominantly employ static congestion control (CC) configurations, yet the heterogeneity of LVS scenarios undermines the efficacy of a uniform CC solution applicable to all users, as evidenced by our production measurements. While learning-based CC approaches show promise, they often suffer from limited generalizability, non-transparent design, and high computational overhead, struggling in large-scale CDNs. In this paper, we propose BIFROST, a new CC architecture grounded in a concerndecoupling paradigm. BIFROST separates fixed control logic from scenario-specific parameter optimization, enabling CDN systems to adapt dynamically to diverse LVS scenarios. To materialize it at CDN scale, BIFROST introduces a bilateral collaboration mechanism that identifies the LVS scenarios of each session by extracting client-side characteristics from viewing requests. It further employs offline model-based optimization to derive more effective control parameters for each scenario. We have deployed BIFROST on Alibaba Cloud’s production CDN for nearly a year, serving a commercial LVS application. BIFROST has markedly improved QoE metrics by 2.7% to 32.1%, reinforcing the competitive edge in the CDN market. We also share our experiences and lessons learned from its large-scale deployment.