Bitrate ladders are getting smarter—and it’s not just about resolution anymore. With modern streaming spanning everything from underpowered devices to GPU-rich endpoints, it’s time for bitrate ladders to adapt to the capabilities of the client, not just the content. Enter DeepStream: a scalable, per-title encoding approach that delivers base layers for legacy CPU-only playback and enhancement layers for GPU-enabled clients, powered by lightweight, super-resolution models fine-tuned to the content itself. DeepCABAC keeps these enhancement networks compact, and smart reuse of scene and frame similarities slashes the cost of training. But training every frame of a video? That doesn’t scale. To make deep video enhancement practical, Efficient Patch Sampling (EPS) pinpoints just the most information-rich regions of each video using fast DCT-based complexity scoring. The result? Up to 83% faster training with virtually no quality loss—saving compute without compromising QoE. And here’s where it gets really exciting: during playback, the client starts learning. When a resolution switch happens, previously downloaded high-res segments are downsampled and used to locally train or fine-tune super-resolution models in real-time—right on the device. No extra bits, no cloud-side retraining. Just smarter, self-improving video playback that adapts to network conditions, device power, and content complexity on the fly. It’s a glimpse into the future of adaptive streaming—intelligent, efficient, and personal. This talk was presented at Demuxed 2025 in London, a conference by and for engineers working in video. Every year we host a conference with lots of great new talks like this – learn more at https://demuxed.com