Personalized Video Streaming and Playback Technology: Methods, Architecture, and Optimization

Short video has become a dominant content format, and ByteDance has built a personalized video technology system that differs from traditional audio‑video pipelines.

The article first reviews the traditional video stack—codecs, streaming protocols, multimedia frameworks, and cloud services—and points out their limitations for short‑video scenarios with massive user and content diversity.

It then introduces a methodology that treats the whole playback sequence as a decision problem, defining an action space, a state space (User, Item, Context), and objective functions that maximize business value while respecting cost constraints.

Key components include personalized streaming & playback, personalized quality scheduling across multiple CDNs, on‑demand delivery, and user‑item aware encoding (UIAE) that selects optimal bitrate ladders for each video based on predicted user preferences and video value.

The system architecture adds a strategy‑center component, a unified framework, and dynamic configuration to enable fine‑grained control of encoding, decoding, scheduling, and resource allocation.

Resource allocation models consider limited and heterogeneous transcoding resources, bandwidth and storage costs, and solve a constrained optimization problem using a streaming‑style batch process.

Extensive online A/B and quasi‑experimental evaluations demonstrate significant gains in playback smoothness, quality, CDN cost efficiency, and overall business metrics.

The paper concludes that a personalized, data‑driven approach can substantially improve short‑video services and is applicable to other media domains such as live streaming.