The Definitive Guide to Context-Aware Encoding

Context-aware encoding is a newer development in the online broadcasting world that is making the streaming process more efficient. This type of encoding is designed to stream with a lower bitrate while maintaining quality, which is a win-win for broadcasters.

In this post, we are going to discuss everything broadcasters need to know about context-aware encoding. We will discuss why you’d use context-aware encoding and how to access it. To wrap things up, we will discuss some streaming technology that is related to content-aware encoding.

Let’s get started by reviewing the basics of encoding.

Table of Contents

• What is Encoding?
• What is Context-Aware Encoding?
• Why Use Context-Aware Encoding?
• How to Access Context-Aware Encoding
• Features and Benefits of Context-Aware Encoding
• Adaptive Bitrate Streaming vs. Context-Aware Encoding
• Latest Technological Advancements and AI Integration (2025)
• Practical Applications of Context-Aware Encoding for Businesses
• The Future of Context-Aware Encoding
• Conclusion

What is Encoding?

Before we get into context-aware encoding, let’s quickly review what encoding is and why broadcasters use it.

En/coding is the process of converting the RAW video file that is captured by a camera into a digital file. RAW video files are bulky since it retains every single frame that is captured, so converting to digital video files is essential if your goal is to stream over the internet.

The encoding process uses a dedicated tool that comes in both hardware and software forms. Software encoders are cheap or free, and they typically are bundled with other innovative streaming software. Hardware encoders are more expensive and are typically used for more advanced streaming setups.

Encoders use a transport technology called codecs to carry video files over the internet. The codecs encode and decode the data to make it lighter and easier to transport in real-time.

Transcoding is a function that is very similar to encoding, and the functions are often thought to be interchangeable. However, there is a difference between the two. Transcoding deals with converting the size of a video file, as opposed to encoding, which converts the type of video file.

The purpose of transcoding is to create multiple renditions, or copies, of a video file in different sizes. This comes in handy for broadcasters that are streaming to an audience with varying internet connections. 

What is Context-Aware Encoding?

Context-aware encoding, which is often shortened to “CAE,” is a newer streaming technology that is designed to optimize the live streaming process. It is built to reduce the video bitrate while maintaining, or even improving, the video’s quality.

This technology was created by Brightcove and was first made available to the public in 2018. Although it has been around for a few years, context-aware encoding still isn’t widely used by the average broadcaster. However, it is used by some of the leading B2C players in the online streaming space.

If you have used Netflix in recent years, you have seen context-aware encoding in action, since it is one of the top users of this technology. Netflix has had great results in both improving its user experience and saving money on storage and bandwidth.

Since CAE optimizes the streaming process by reducing the bitrate of content that is streamed, it also conserves bandwidth and storage space. Since bandwidth and storage are costly for professional broadcasters, especially those with large audiences, context-aware encoding can help save money.

As a refresher, bitrate is the among of data transferred over the internet in a given period of time. Typically, higher resolution correlates with a higher bitrate. However, with context-aware encoding, this is not the case. CAE makes it possible to stream higher-quality content at a lower bitrate.

These improvements are made possible by artificial intelligence-like technology that observes the context of the stream for each and every viewer. The main context that the technology is looking for is the strength of the users’ internet network and their devices’ ability to stream.

Context-aware encoding operates via cloud-encoding technology and sets a variety of encoding parameters to suit each viewer’s unique context. This is how CAE conserves bandwidth.

What makes streaming with context-aware encoding so impressive is that it is carried out almost instantaneously. The context is assessed and addressed all in real-time. The latency caused by CAE is a single frame, which is quite impressive.

Why Use Context-Aware Encoding?

Context-aware encoding is very valuable for broadcasters because it helps make video transport more efficient. This efficiency is beneficial to viewers and broadcasters alike.

CAE also helps to improve the user experience since it reduces buffering and speeds up start times. These improvements make things much more seamless on the viewers’ end.

Since CAE streams in a lower bitrate, it helps to conserve bandwidth. Since bandwidth is a major cost associated with professional broadcasting, this is certainly a win. In fact, using context-aware encoding can reduce your video storage and stream delivery costs by as much as 50%.

How to Access Context-Aware Encoding

Unfortunately, context-aware encoding is not yet easily accessible. Even though it was made “mainstream” in 2018, there are still developments that need to be made with supporting technology before it is widely accessible.

Context-aware encoding can only be accessed as integration with some online video platforms. Currently, only a few platforms support CAE, including Brightcove and Kaltura.

In order to access context-aware encoding on either of these streaming platforms, users must pay additional add-on fees to access the specialized integrations.

Adaptive bitrate streaming, which is similar to context-aware encoding, is a more accessible option for broadcasters that want similar benefits to context-aware encoding. Dacast and many other online video platforms support this technology.

Features and Benefits of Context-Aware Encoding

Dynamic Bitrate Ladder Creation

Context-Aware Encoding (CAE) now enables custom bitrate ladders tailored to each video’s complexity. Unlike traditional fixed bitrate ladders, this AI-driven approach optimizes video quality while significantly reducing storage and bandwidth costs. By dynamically adjusting encoding settings, CAE ensures an efficient balance between quality and data usage, leading to video streaming cost reduction of up to 50%. Businesses can improve streaming efficiency without sacrificing viewer experience, making this technology a game-changer in 2025.

FastPix

FastPix enhances CAE by using artificial intelligence in video encoding to analyze video content in real-time. This AI-powered system identifies high-motion and complex scenes, applying advanced compression techniques to maintain visual quality while reducing data consumption. As a result, streaming providers can deliver sharper, more efficient streams with minimal buffering.

Enhanced Viewer Engagement

CAE also improves viewer experience enhancement by personalizing content delivery. AI analyzes viewer preferences and network conditions to optimize video streams for each user. This approach not only enhances quality but also boosts retention by ensuring a seamless, engaging experience across all devices.

Adaptive Bitrate Streaming vs. Context-Aware Encoding

It is impossible to discuss context-aware encoding without acknowledging adaptive bitrate streaming. The two have some overlap that are certainly worth mentioning.

Let’s take a look at how adaptive bitrate streaming and context-aware encoding are related.

What is Adaptive Bitrate Streaming

Adaptive bitrate streaming, which is often called “ABR streaming,” is the technology that automatically deploys the optimal rendition of a video to each user based on their internet. ABR streaming is made possible with the HLS and MPEG-DASH protocols. ABR streaming requires the support of an adaptive video player.

ABR streaming determines the appropriate rendition based on the viewers’ internet connection, playback window size, and whether or not there is buffering. This information is retrieved as soon as the viewer clicks the “Play” button. 

Some streaming setups support dynamic adaptive bitrate, which means that the bitrate fluctuates throughout the stream based on the viewers’ conditions.

Adaptive bitrate streaming uses a structure that is often referred to as a “ladder.” The encoding criteria for all of the renditions are predetermined. That means that there is a set bitrate for different resolutions. Each rendition is pulled from the ladder of encoded renditions rather than being encoded on a case-by-case basis.

How CAE and ABR Compare

Content-aware encoding and adaptive bitrate streaming technology work hand-in-hand in some streaming setups. Many professionals in the industry would consider ABR streaming a step on the path to content-aware encoding. However, there are some differences between the two.

The idea behind context-aware encoding is rooted in the same concept that adaptive bitrate streaming uses. They share the basic functionality of sending the optimal rendition of the video to each viewer based on their internet connection.

However, the key difference is that CAE encodes every rendition for each unique viewer, and ABR streaming uses predetermined encoding criteria. This means that ABR is less intuitive and involved than CAE, which makes it a bit less powerful.

Another place where adaptive bitrate streaming is lacking is that it is not as efficient as context-aware encoding. CAE and adaptive bitrate streaming both focus on enhancing the viewer experience by sending the optimal rendition of the video file. However, adaptive bitrate streaming is not as optimal or efficient from the broadcaster’s perspective.

Adaptive bitrate streaming does not conserve bandwidth or storage usage as context-aware encoding does. Therefore, it does not have the same money-saving benefits.

At this point, the only real leg up that adaptive bitrate streaming has over context-aware streaming is that it is more easily accessible. Luckily, it is a good start for broadcasters who want to improve their user experience.

Latest Technological Advancements and AI Integration (2025)

AI-Driven Encoding Algorithms

Recent advancements in artificial intelligence are reshaping how video content is encoded and delivered. AI-driven encoding algorithms now use content analysis algorithms to assess video complexity in real time. These algorithms dynamically adjust encoding parameters, ensuring the best possible quality while reducing processing power demands. Unlike traditional methods that rely on fixed adaptive bitrate ladders, AI-driven encoding optimizes for both high and low-motion scenes, cutting down unnecessary data transmission. This innovation improves bandwidth optimization, lowers storage costs, and enhances overall streaming efficiency.

Real-Time Multimodal AI

The integration of AI into real-time streaming has introduced context-aware encoding solutions that go beyond simple bitrate adjustments. By analyzing multiple data types—such as audio cues, facial expressions, and scene changes—AI creates dynamic delivery strategies tailored to viewer needs. This approach ensures smoother streaming experiences, even in varying network conditions. Unlike cloud encoding technology that applies uniform settings across all content, AI-powered CAE (context-aware encoding) dynamically refines streaming quality on a per-frame basis. This leads to significant reductions in storage costs and improved viewer engagement, positioning businesses to scale their video streaming operations efficiently.

Practical Applications of Context-Aware Encoding for Businesses

Cost Reduction

Implementing Context-Aware Encoding (CAE) can lead to significant video streaming cost reduction. By dynamically adjusting encoding settings based on content complexity and viewer conditions, businesses can cut storage and bandwidth expenses while maintaining high-quality video. This optimization helps companies lower their streaming costs without sacrificing performance.

Improved Quality of Experience (QoE)

CAE enhances viewer experience by reducing buffering and improving load times. Unlike traditional encoding methods, CAE fine-tunes video streams in real time, ensuring smooth playback across different devices and network conditions. This viewer experience enhancement is crucial for businesses that rely on live streaming to engage audiences and generate revenue.

Scalability

For companies looking to expand their reach, CAE offers a scalable approach to streaming efficiency. It adapts to various network environments and device capabilities, ensuring consistent quality for global audiences. This adaptability is especially useful for platforms that serve users in regions with varying internet speeds.

Competitive Landscape and Adoption

Industry Adoption

Leading video platforms like Brightcove and Kaltura have embraced artificial intelligence in video encoding, integrating CAE to optimize streaming workflows. Their adoption highlights the growing industry recognition of CAE’s value.

Emerging Tools

Companies such as FastPix have developed AI-driven solutions for custom bitrate ladder creation. These tools analyze video content to create optimized encoding profiles, balancing quality and cost savings. As CAE continues to evolve, businesses can expect even greater efficiency and performance improvements.

The Future of Context-Aware Encoding

Although context-aware encoding is not widely used yet, there is hope that it will one day become more popular once other related streaming technology catches up. The benefits of context-aware encoding are undeniable, so moving in the direction of this technology only seems reasonable.

We’ve seen that popular online video platforms, including Brightcove and Kaltura, have embraced context-aware encoding. It is reasonable to assume that other similar online video platforms will eventually follow suit.

Also, the fact that Netflix is using the technology is promising since this company is a well-known leader in the online streaming space.

Other than the delay in the development of complementary technology, there don’t seem to be any other roadblocks to the rise of context-aware encoding.

Integration with 5G Networks

The rollout of 5G technology is set to improve Context-Aware Encoding (CAE) by offering higher bandwidth and lower latency. This will make live streaming more efficient, allowing businesses to deliver high-quality content with fewer interruptions. CAE can dynamically adjust video quality based on real-time network conditions, ensuring a seamless experience even in areas with fluctuating connectivity. With 5G, streaming efficiency improves, helping businesses reduce costs while enhancing viewer experience.

Advancements in AI

Artificial intelligence in video encoding continues to evolve, making CAE even smarter. In 2025, AI-driven encoding algorithms will better predict network conditions and viewer behavior, optimizing bitrate settings automatically. This means businesses can create custom bitrate ladder configurations tailored to audience needs, improving video streaming cost reduction while maintaining top-tier quality. As AI refines encoding precision, companies can minimize storage and delivery expenses while maximizing viewer engagement.

Conclusion

Context-aware encoding is quite innovative, and its development is indicative of an equally innovative future in live streaming. It is a great resource for broadcasters who are looking to improve the quality of their online videos and their overall user experience.

Although context-aware encoding is not very accessible at this time, we are hopeful for a future where it is better integrated with the online broadcasting world. In the meantime, we encourage broadcasters to seek solutions that use adaptive bitrate streaming.

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