Technical question about video bitrate optimization for large-scale YouTube workloads

[Leonardo Gemen]

Dear YouTube Video Infrastructure Team,

My name is Leonardo Gemen and I closely follow developments in large-scale video processing and streaming infrastructure.

Given YouTube’s enormous video processing scale, I was curious whether your team has evaluated content-adaptive bitrate optimization technologies such as Beamr Imaging’s CABR approach as a potential complement to existing encoding pipelines.

Beamr claims their optimization layer can reduce video bitrates by up to 30–50% while preserving perceptual quality, which could theoretically reduce storage, CDN, and processing costs at very large scale.

From a technical standpoint I would be interested to understand:

• Whether YouTube evaluates external optimization layers on top of modern codecs such as AV1.
• If adaptive bitrate optimization methods can still produce meaningful savings when operating at hyperscale.
• Whether technologies similar to CABR have been considered for large video libraries or AI-video datasets.

I completely understand that internal architecture details cannot be shared, but any high-level perspective on how YouTube approaches large-scale bitrate optimization would be greatly appreciated.

Thank you for your time.

Kind regards,
Leonardo Gemen

Verstuurd vanaf mijn iPhone