Brightcove's Yuriy Reznik Talks 2022 Emmys and Context-Aware Recording
Tim Siglin: Welcome back to Streaming Media East 2022 here at the Westin Copley in Boston. Today, I've got with me Yuriy Reznik, who's the VP of Research at Brightcove. The Brightcove is, of course, a Boston-based company, but Yuriy, you're based out in Seattle. So, some pretty exciting news for you this year: the two Emmys that you won. A lot of people in the industry were shown holding one Emmy, and I saw a picture of you with two Emmys. What were the Emmys for?
Yuriy Reznik: Well, it was indeed a very humbling moment, and Brightcove was honored by two Emmy awards from National Academy of Television Arts and Sciences. One was in the category of massively parallel workflows for media processing. And that reflects work that's been done at Brightcove since very early beginnings when we started working on massive transcoding processes, building products, such as Zencoder and Brightcove Studio that was even before cloud systems existed. We did it in our data centers an eventually we transitioned to public clouds, to AWS and others. And the second Emmy was in the category of perceptual metrics for optimized 3D encoding. And that reflects work that we have done in developing Brightcove context-aware encoding (CAE). It's a technology for optimizing transcoding of video for streaming applications, with multiple bitrates and resolutions chosen for encoding. And we do it in a way that is preserving the best possible quality as it send this composition of streams to the end users over CAE.
Tim Siglin: And CAE is where I first met you a number of years ago. One of the things that seems fascinating for you from a Brightcove perspective is you get to see end-to-end from the encoding through to delivery. How do those different parts in the middle sort of feed into decisioning around CAE?
Yuriy Reznik: Excellent question. So Brightcove is really in a unique position by looking at the problem holistically. We ingest videos, we transcode videos. We choose CDNs to use. We have on origin servers connections to CDNs, and then our own players. And we have analytics systems that collect data from both players, as well as CDN and analytics data. We see network statistics, we see user statistics and the shapes of those networks' distributions is what we are using to drive CAE. And if you look at it, end-to-end CAE effectively works as an end-to-end optimization system. It's not only content-aware encoding; it's a context-aware encoding where we take into account not only properties of content, but also properties of networks, different devices that receive it and the plurality of network distributions that they see.
Tim Siglin: And so just to make sure that people understand, while there are systems out there that focus on content-aware, meaning same perceptual quality at slightly lower bit rates, in reality, what you're doing is taking that plus what the network topology is at any given moment and making the decisions around the transcoding.
Yuriy Reznik: That's correct. In this scenario, we are looking at network statistics that are more or less steady state, which we collect based on previous deployments for the same customers, the same audiences that they have. And it makes perfect sense. For example, if the predominant receiving devices are mobiles, then those will be low bitrate renditions. That's where you need to put more of them and you need to optimize their resolutions and bitrates to be effective in that operating range. On the other hand, if you have broadband connections, it will be the highest renditions all the time. That's only rendition you would get in that case.
Tim Siglin: And it's interesting because it seems like oftentimes, what you hear from people is, "Well, you never know who's gonna watch your content. You need to go ahead and transcode it. All of these renditions are fine." Obviously, there's a storage component to that. There's a processing component to it. But if you know through real-time data that you don't have people watching those other renditions, you don't necessarily have to go through that.process.
Yuriy Reznik: But what's important is the shape of the probability distributions, because those probability distributions are what allows us to shape our better locations most efficiently. And that's a very unique aspect of what we do relative to other solutions.
Tim Siglin: Hence the Emmys. Well, Yuriy, thank you very much for your time. Always good to talk to you. And we'll be right back.
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