Can you elaborate on the "novel anomaly-detection algorithms" used here?

Sure! We evaluated several anomaly-detection tools & libraries. They included:

tried-and-true statistical methods like probability density functions

Yahoo EGADS anomaly-detection library

Numenta HTM neural-network anomaly-detection library

We ruled out HTM due to AGPL licensing concerns. It's an interesting product, but wasn't a good fit for us at this point in time. EGADS and other basic statistical methods can actually get you pretty far.

And what do you do with this video anomaly information?

Mux (https://mux.com/) collects performance metrics for video delivery & playback on the websites & apps of our customers. These metrics feed into our real-time alerting system. If the error-rate for a customer property (site) or video-title is exceptionally high then an alert will be triggered. Mux customers can configure alert notifications to be sent to Slack & email, and view a history of alerts in the Mux web dashboard. We also offer the ability to view breakdowns of playback failures, video start-up times, and more through our dashboard. This can be helpful for diagnosing playback issues related to specific browsers, geographies, ISPs, and more.