NAB Show Perspectives: Enhancing streaming efficiency with AI-driven encoding

The increasing demand for high-quality video streaming has placed significant pressure on content providers to optimize their workflows while controlling costs. Bandwidth consumption, storage limitations, and encoding inefficiencies are among the biggest challenges for streaming platforms today. To address these issues, artificial intelligence (AI) and Content-Adaptive Encoding (CAE) have emerged as game-changing technologies. By dynamically adjusting encoding parameters based on content complexity, AI-driven CAE enhances operational efficiency without compromising visual quality.

AI and content-adaptive encoding, a smarter approach to video optimization

Unlike traditional encoding methods that apply fixed bitrate settings across all video segments, CAE leverages AI to analyze video content at a granular level, assigning optimal encoding parameters based on scene complexity. This approach significantly reduces file sizes and bandwidth usage while ensuring a seamless viewing experience. Netflix pioneered this method between 2015 and 2018, achieving over a 30% reduction in bitrate without noticeable quality loss, as measured by the Video Multi-Method Assessment Fusion (VMAF) metric. However, early implementations required extensive computational power, making them challenging to scale.

AI-powered solutions, such as VisualOn Optimizer, have since refined this technology, offering near-optimal efficiency with significantly lower computational overhead. These innovations are now being widely adopted by streaming providers looking to enhance video quality, reduce operational costs, and improve delivery efficiency.

Real-world applications: Case studies in streaming optimization

EiTV, a leading digital entertainment provider, faced rising bandwidth costs and needed an encoding solution to reduce file sizes while preserving quality. By integrating VisualOn Optimizer, EiTV cut video bitrates by 40%, significantly lowering CDN costs without infrastructure changes. As an FFmpeg plug-in, implementation was seamless, minimizing workflow disruption. It took all of two weeks for EiTV to put VisualOn Optimizer into production.

Similarly, Intigral, a top media provider in the MENA region, sought to enhance its VOD workflow without hardware upgrades (include link to PR). VisualOn Optimizer delivered 40% average bitrate savings, peaking at 70%, without compromising quality, as verified by VMAF and blind tests. It also improved KPIs such as startup time and buffering ratio. The solution also eliminated costly multi-pass encoding, cutting computational overhead and energy use.

The role of AI in next-generation streaming workflows

Beyond cost savings, AI-driven CAE is shaping the future of streaming efficiency by enhancing adaptive bitrate streaming (ABR), real-time optimization, and sustainability efforts. In collaboration with Axinom, Intel, and Quanteec, VisualOn is advancing AI-powered encoding technology to further improve efficiency across different streaming environments.

A recent per-scene encoding comparison with Axinom highlighted the effectiveness of AI-driven bitrate allocation, demonstrating how VisualOn Optimizer reduces bandwidth usage while maintaining perceptual video quality. This study reinforced the value of per-frame analysis in modern encoding workflows, showing that intelligent bitrate allocation can significantly enhance video delivery efficiency.

Meanwhile, a collaboration with Intel (site source here) showcased how VisualOn Optimizer running on Intel hardware can reduce encoding bitrates by up to 40% while enhancing visual quality. By leveraging Intel’s advanced processing capabilities, the solution achieves a balance between efficiency and computational performance, making AI-powered CAE more accessible to large-scale streaming providers.

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Additionally, VisualOn’s collaboration with Quanteec focuses on sustainable streaming solutions. By reducing bandwidth requirements, AI-driven encoding minimizes energy consumption and infrastructure strain, contributing to a more eco-friendly streaming ecosystem. These efforts align with industry-wide goals to develop scalable, cost-efficient, and sustainable video delivery technologies.

Transforming streaming efficiency with AI-powered encoding

As streaming platforms continue to expand, AI-powered Content-Adaptive Encoding is proving essential for optimizing efficiency. Providers that integrate AI-driven solutions benefit from reduced bandwidth consumption, improved encoding workflows, and enhanced scalability—all while maintaining high visual fidelity. The ability to achieve substantial cost savings and sustainability benefits makes CAE a critical component in the next generation of video streaming.

By implementing advanced encoding technologies such as VisualOn Optimizer, companies like EiTV and Intigral have successfully improved their streaming operations without incurring additional hardware costs. As AI-driven per-frame encoding, adaptive bitrate streaming, and real-time optimization continue to evolve, the industry is moving toward a more efficient, cost-effective, and high-quality video streaming future.

Yang Cai, VisualOnYang Cai is the president and CEO at VisualOn, the most trusted client streaming media solution provider for top media companies worldwide. Yang holds a Ph.D. degree from The University of Texas at Austin in the field of study Computer Science.