Three years ago, Netflix asked a deceptively simple question: What would it take to stream live events with the same quality, scale, and reliability as our on-demand catalog? What followed wasn’t a moonshot. It was a methodical, multi-year buildout that turned Netflix into a serious live platform that now supports everything from comedy specials and NFL games to record-setting boxing …

I just launched Streaming Monetization 101, a 50+ lesson, roughly seven-hour course created in partnership with the Streaming Video Technology Alliance (SVTA). It’s available now through SVTA University for $399. The course provides new hires in streaming services and the companies that support them with a comprehensive, real-world understanding of how platforms generate and grow revenue. It covers the business …

On July 3, I spoke with Hari Kalva and Velibor Adzic from Florida Atlantic University about Feature Coding for Machines (FCM), a new MPEG standard being developed for machine-to-machine video applications. You can watch the full interview on YouTube and it’s embedded below. This blog presents that conversation in a lecture-style format. It follows the slides shown during the session …

AI video compression standards have moved from the research lab to the standards committee. This post summarizes the current status of formal standardization efforts as of July 2025, the groups driving them, and the anticipated timeline for real-world deployment. If you’re wondering whether the next generation of video codecs will be AI-native or just AI-enhanced, this should give you a …

Dan Rayburn recently published the video and slides from my NAB Streaming Summit session, where I walked through real-world techniques to optimize x264 and x265 for quality and efficiency. No AI, no codecs from 2030, just practical optimizations that work today. If You’re Still Encoding with x264 and x265—Good You don’t need to jump to AV1 or VVC to get …

As third-party cookies fade into digital history, first-party data has risen to the top of every marketer’s wish list. Touted as the ultimate solution for targeting, personalization, and measurement in a privacy-centric era, first-party data is often positioned as the “panacea” for the challenges left in the wake of cookie deprecation. But beneath the optimism lies a hard truth: first-party …

I recently tested the Deep Render AI codec and issued a report, which you can read here. The bottom line was that in the tested low-latency use case, the Deep Render AI codec substantially outperformed SVT-AV1 quality-wise and was only slightly behind VVenC. While the codec lacks features like bitrate control that are necessary for most deployments, it offers outstanding integration …

Streaming Media recently published my article on VVC and AV1, Software Decoding and the Future of Mobile Video. An honest evaluation of the article might observe that while the quality comparisons between SVT-AV1 and VVenC were relatively complete, the article didn’t share any mobile playback performance data. That’s because I couldn’t find any VVC players for testing on either mobile platform.  …

Recently, I reviewed the Deep Render AI codec and noticed a substantial disconnect between subjective and objective results. Subjective testing showed Deep Render with a 45 percent BD-Rate advantage over SVT-AV1. VMAF showed just 3 percent. While subjective evaluation has always been the gold standard, this gap forced a more basic question: how accurate are traditional objective metrics when applied …

While many AI-based codecs are still making their first appearance in white papers, often with tortured playback requirements and no working decoder, the Deep Render codec is already encoding in FFmpeg, playing in VLC, and running on the billions of NPU-enabled devices already in the market. Let’s take a step back. I’ve been following the development of the Deep Render …