As UHD, HDR, live sports streaming, immersive audio and even 8K experimentation move into the mainstream, encoding has become a core business strategy. Broadcasters and streaming providers must elevate the viewer experience while reducing bandwidth and infrastructure costs.
Advances in AI-driven optimization, content-aware encoding and next-generation codecs enable operators to deliver higher-quality video at lower bitrates — fundamentally reshaping the delivery of premium video experiences.
Encoding as a Strategic Business Driver
Every additional megabit per second carries a cost — in CDN fees, transport, storage and processing power. At scale, even marginal bit rate reductions translate into substantial operational savings. Conversely, any visible drop in video quality risks churn, particularly in today’s competitive market where viewers can instantly switch services.
The challenge is inherently complex. Service providers must optimize three compression variables simultaneously: video quality, bitrate efficiency/processing power and latency. Improvements in one area often affect another. For example, reducing latency can come at the expense of the bit rate efficiency. Improving video quality by keeping bit rate low can increase computational load. Adding immersive formats increases complexity across the pipeline.
Modern encoding strategies recognize and treat compression as part of the overall delivery strategy, not just a codec setting.
The Rise of AI and ML Encoding Innovations
One of the most significant encoding developments in recent years has been the integration of machine learning into the encoding workflow. Several key enhancements are enabling broadcasters and service providers to deliver higher video quality, lower latency and greater efficiency.
Content-aware encoding
An advanced technique, content-aware encoding identifies visually important regions within video content — such as faces, text overlays or high-detail textures like grass — and prioritizes them for perceptual quality, (Fig. 1). Rather than treating every frame equally, content-aware encoding analyzes content characteristics in real time and allocates bits where they matter most.
Less critical areas receive fewer bits, preserving overall bandwidth while maintaining viewer satisfaction. Sophisticated rate-control algorithms can deliver significant bitrate savings, in some cases up to 50% without visible quality loss.
Real-time VMAF prediction
Today’s advanced encoding solutions can estimate perceptual quality metrics such as Video Multimethod Assessment Fusion (VMAF) during live encoding, enabling service providers to detect potential degradation before it reaches viewers. Real-time VMAF prediction models can achieve high correlation with offline measurements, up to 95%, allowing accurate quality assessment in live workflows and preventive encoding adjustments.
Automated quality analysis
Embedding AI into quality monitoring shifts service providers from reactive troubleshooting to proactive quality management. AI-driven regression testing and automated quality analysis enhance reliability by identifying deviations across nightly and weekly test streams. The result is a more resilient encoding pipeline where quality is continuously optimized. Service providers benefit by delivering better perceptual quality at lower bit rates, reducing distribution costs.
Intelligent node rebalancing
AI-driven algorithms assess system load, content complexity and processing demands to guide dynamic node rebalancing. This encoding approach enables more consistent resource allocation and stable video quality across distributed deployments.
GPU enhancements
GPU-accelerated enhancements play a pivotal role in the next generation of encoding. By integrating AI-driven pre-processing (like superscaling, denoising or deinterlacing) and GPU-enabled encoding control (like fine-grained Quantization Parameter -QP- control into the GPU pipeline), modern encoding platforms can deliver significant gains in performance and efficiency.
Preparing for the Next Generation of Codecs
While AI optimizations improve encoding efficiency within existing standards, broadcasters and service providers must also prepare their workflows and infrastructure for next-generation codecs.
Scalable encoding pipelines — capable of supporting multiple codecs, base layers and enhancement layers — allow gradual transitions aligned with market and business demands.
Versatile Video Coding (VVC) promises up to 50% bitrate savings over HEVC while maintaining exceptional visual quality and is the selected codec for next-gen broadcasting standards like DTV+. Historically promoted as a royalty-free codec alternative, AV1 continues to gain momentum in OTT ecosystems with an improved efficiency compared to legacy codecs. And Low Complexity Enhancement Video Coding (LCEVC) offers a scalable enhancement layer that can improve compression efficiency without requiring full codec replacement.
Audio codec innovation further expands the scope of modern encoding platforms. Object-based formats such as MPEG-H and Dolby AC-4 enable immersive, personalized experiences. Dialog separation and accessibility features enable broadcasters and service providers to deliver personalized audio experiences to audiences. Moreover, support for object-based metadata for both MPEG-H and AC-4 enables precise audio rendering and personalization.
To accommodate for all these changes, a key strategic consideration for encoding is flexibility. Broadcasters and service providers cannot afford disruptive, large-scale infrastructure replacements every few years. Scalable encoding pipelines — capable of supporting multiple codecs, base layers and enhancement layers — allow gradual transitions aligned with market and business demands.
Powering Next-Gen Video with High Density, Low Latency and Immersive Readiness
Delivering next-generation video experiences requires broadcasters and service providers to handle intensive workloads with precision and reliability. Advanced encoding architectures are being designed for high-density and error-resilient performance. This, in turn, is laying the foundation for higher resolutions, lower latency, immersive formats and emerging viewing experiences.
Certain applications such as live sports streaming highlight why high-performance encoding architectures are essential. For instance, live sports streaming and interactive applications require high quality and a low degree of latency. Optimized pipelines reduce glass-to-glass delay while maintaining compression efficiency. This is essential for betting integrations, synchronized second-screen experiences and social engagement.
At the same time, experimentation with 8K and immersive video formats is accelerating. Encoding technology providers like Harmonic are trialing OTT profile ladders derived from an 8K source stream, processed in the cloud using both CPU and GPU resources. The profile ladder showcased in Figure 2 was processed in the cloud and would have been cost prohibitive two years ago. These trials illustrate the industry’s move toward higher resolutions delivered efficiently through hybrid compute architectures.
Even if 8K remains niche in the near term, the underlying engineering advances — high-density processing, scalable cloud-native workflows and error-free multi-profile generation — lay the groundwork for spatial computing, VR and headset-based experiences.
The New Compression Imperative
Ultimately, the latest encoding innovations enable broadcasters and service providers to deliver superior video quality at lower bitrates while reducing costs. Content-aware encoding, AI advancements and emerging codecs all have a role to play in helping service providers deliver premium experiences with the utmost efficiency.
In an era defined by subscriber churn, cost cutting and relentless viewer expectations, video compression remains a strategic necessity. Service providers that treat encoding as a core priority will be best positioned to thrive in the next phase of video evolution.
