Why use the same encoding for every video? This is what Context-Aware Encoding (CAE) answers. Every video is different, so why not adjust the settings to fit each one? Using the same encoding for all videos can waste resources and hurt quality. CAE allows us to change encoding based on things like the video's complexity, the viewer's device, and the network speed.
In this blog, we’ll explain what CAE is, how it works, how it improves on older methods like Adaptive Bitrate Streaming, and compare CAE with Content-Aware Encoding. We’ll also discuss why CAE is important for developers and content providers who want to improve video quality while saving resources.
Video encoding is the process of converting raw video files into a compressed format, making them easier to store, share, and play on various devices. If you're new to the concept, you can dive deeper into video encoding in our beginner’s guide here.
Context-Aware Encoding is a video encoding technology that creates custom bitrate ladders for each video, taking into account the content, device type, and network speed of the viewer.
It improves video quality by determining the ideal number of quality options (renditions), along with the appropriate resolutions and bitrates for each, based on the viewer’s device and internet connection. This ensures smoother playback across various devices and network speeds, while also lowering storage requirements and reducing the amount of bandwidth needed for streaming.
Context-Aware Encoding delivers the same quality as a traditional static ABR ladder, but with half the number of renditions. By using lower bitrates or higher resolutions for each rendition, it significantly enhances playback performance and reduces costs.
Context-aware encoding (CAE) is a smart technology that optimizes video streaming by adjusting how videos are encoded based on various factors. Here’s a simple breakdown of how it works:
Once the video is encoded with its custom bitrate ladder, the best version is delivered to viewers based on their current network and device capabilities, providing a smooth streaming experience.
Context-Aware Encoding (CAE) offers several advantages that can make video streaming better for both viewers and content providers:
Adaptive Bitrate Streaming (ABR) is a widely used technology for streaming videos smoothly. It adjusts video quality in real time based on the viewer's bandwidth and device, ensuring minimal buffering and a seamless experience. However, traditional ABR methods have room for improvement, and Context-Aware Encoding (CAE) offers a smarter, more efficient solution.
ABR streams video content from multiple renditions at different bitrates and resolutions, which are then divided into small segments. The video player switches between these resolutions in real time based on the viewer's network speed. For example, when the network is fast, it selects a higher resolution, and if the connection slows, it switches to a lower resolution to keep playback smooth.
While this adaptability ensures minimal buffering and a seamless experience, the underlying process depends on a universal "encoding ladder" that does not differentiate between content types. Whether it's a high-action sports clip or a simple animation, the same bitrate rules apply, leading to inefficiencies.
Traditional ABR works well but often uses a standard encoding ladder for all types of content. This approach can cause several issues:
The problems with traditional ABR can be fixed by using Context-Aware Encoding (CAE), which makes video delivery more efficient.
With CAE, you can expect:
For example, a typical one-minute video that would normally take up 70 MB of storage space using traditional encoding methods.
Using context-aware encoding:
Now, by using CAE the total storage required drops to approximately 23 MB, representing a 66% reduction in storage costs compared to traditional methods.
What’s the result?
You can see total savings of up to 50% on both storage and bandwidth costs, making your streaming process more cost-effective without sacrificing quality. Start saving today with CAE, and let your video content reach viewers more efficiently, all while keeping your expenses down.
At FastPix, we understand the importance of both quality and cost-efficiency when it comes to streaming. That’s why we’ve video technologies like Context-Aware Encoding (CAE) pre-build into our system, helping you reduce streaming costs while maintaining an exceptional viewing experience.
With FastPix, you can easily leverage the power of CAE to:
Make the smart choice with FastPix. Save more, stream better.
While Context-Aware Encoding is optimized for pre-recorded videos, its application in live streaming is limited by the time required for video analysis. However, pairing it with fast encoding tools or real-time prediction models can enhance its suitability for live content.
Yes, Context-Aware Encoding is designed to be flexible. By leveraging machine learning and data analytics, it can integrate advancements like 8K streaming, immersive content (AR/VR), or improvements in network protocols like 5G to further optimize video delivery.
Small-scale platforms can use cloud-based services offering Context-Aware Encoding, which eliminates the need for expensive infrastructure. By reducing storage and bandwidth usage, the technology ensures cost-efficiency without compromising video quality.
Testing typically involves comparing video quality metrics (like PSNR or SSIM) and playback performance across various devices and network conditions. A/B testing with user feedback also helps validate improvements in streaming quality and resource efficiency.
Developers can integrate Context-Aware Encoding by using APIs or software development kits (SDKs) provided by encoding service providers. Careful configuration of video analysis modules and storage management systems is essential for seamless implementation.
Yes, Context-Aware Encoding is designed to be flexible. By leveraging machine learning and data analytics, it can integrate advancements like 8K streaming, immersive content (AR/VR), or improvements in network protocols like 5G to further optimize video delivery.