ML Video Codec (MLVC): Research to Product#

Nabakumar Khongbantabam (Microsoft)

• Why: A rare research-to-product report for learned video compression in a mainstream product (Teams). The tradeoff map — latency, compute cost, hardware acceleration, compatibility, quality measurement — is the same one anyone faces shipping ML into an existing heterogeneous client fleet.
• What: The product and engineering considerations behind developing and scaling ML Video Codec (MLVC) in Microsoft Teams, and what it takes to turn a promising compression model into durable product impact. (from abstract only)
• How: Not detailed in the abstract; the framing centers on navigating tradeoffs between quality gains and the operational costs of ML codecs across diverse devices and network conditions.
• Where applicable: Microsoft Teams production environment; heavily dependent on device heterogeneity, hardware availability for inference, and codec compatibility constraints. Not an infrastructure talk — the transferable value is the decision framework, not the codec itself.