Six Ways to Use AI in Video Streaming

In recent years, AI video streaming has gained a lot of attention from internet users, particularly due to the several benefits it provides over other traditional methods of media delivery. While this may sound alien-ish, it’s actually not. Video streaming has been around for a while and the reason for its popularity isn’t farfetched.

Video streaming allows users to access and watch videos instantly without downloading large files, providing a convenient and accessible way to consume content on-demand or live, with features like interactive chat and real-time participation, all while reaching a global audience across various devices.

The rapid advancements in AI over the past four to five years have brought innovative changes to video streaming, making it more personalized, efficient, and engaging. AI video streaming is transforming content delivery, including improving video quality, enabling real-time interaction, enhancing visual effects, and delivering personalized content recommendations.

In this article, we’ll explore six efficient ways to leverage AI video streaming and how Cloudinary’s tools can help simplify and enhance these processes.

In this article:

• 1. AI-Driven Video Quality Optimization
• 2. Content-Based Recommendations
• 3. Automated Transcoding and Format Conversion
• 4. Real-Time Video Content Moderation
• 5. Enhanced Video Search and Metadata Tagging
• 6. Real-Time Language Translation and Subtitling
• How Cloudinary Simplifies AI-Driven Video Streaming

1. AI-Driven Video Quality Optimization

AI video enhancement or upscaling relies on deep learning algorithms, especially convolutional neural networks (CNNs). These algorithms upscale low-quality videos by predicting the missing information (pixels) in a video based on the surrounding pixels and learned patterns. One of the greatest superpowers of AI is its versatility. In real-time video streaming, AI can be used to automatically upscale or downscale video quality based on network conditions, user device capabilities, and bandwidth, ensuring smooth playback even in low-bandwidth conditions.

2. Content-Based Recommendations

Have you ever wondered why your social media feed shows videos closely related to the ones you just watched? That’s the magic of a recommender system at work. Essentially, recommender systems are specialized machine learning algorithms designed to suggest relevant contents to users based on their preferences, behavior, or other contextual information. These systems aim to personalize user experiences by predicting what might interest them. For example, streaming services like Netflix and Amazon Prime Video use recommender systems in recommending movies and shows based on a user’s past viewing pattern.

AI-driven content-based recommendation provides several benefits in video streaming, such as:

• Increased viewer retention and satisfaction by delivering content tailored to individual tastes.
• Identify niche interests and recommend content that users might not have discovered otherwise, expanding their viewing opportunities.
• Tracking user engagement with recommended content so businesses can better understand their users’ viewing behaviour to optimize their content library and delivery strategies.

3. Automated Transcoding and Format Conversion

Video transcoding is the process of converting a video file from one format to another, often by adjusting parameters such as resolution, encoding, and bitrate, to make it more compatible or to reduce its file size.

In video delivery, transcoding is useful for a number of reasons, including:

• Transcoding optimizes video accessibility for mobile devices and users on slower internet connections, improving user experience and improving content reach.
• Video transcoding enables playback, allowing videos to play on devices that don’t support certain codecs.
• Streaming services and media companies can minimize the cost of infrastructure as optimizing a video by compressing it reduces the costs of transferring or storing it.

The process of video transcoding is complex and computationally intensive, however, AI video streaming simplifies it by using machine learning algorithms to analyze each video and determine the optimal settings for transcoding and converting videos into different resolutions and formats.

4. Real-Time Video Content Moderation

AI video recognition is an emerging technology. This technology uses Convolutional Neural Networks (CNNs), a type of machine learning algorithm to automatically detect and flag inappropriate or unwanted content in video streams, helping maintain a safe and compliant streaming environment. Content moderation tools powered by AI can identify objectionable material, such as violence or explicit content, and apply automated filters or flag it for review.

For example, Google Cloud Video Intelligence is a service that makes it easy to add AI video moderation to your applications. Cloudinary provides an add-on for Google’s automatic video moderation service, fully integrated into Cloudinary’s video management and transformation pipeline.

5. Enhanced Video Search and Metadata Tagging

AI metadata tagging is a technique that uses machine learning to automatically analyze and label digital content. This makes it easier to organize, search, and understand data. Using AI in video search and metadata tagging provides benefits, such as:

• Automating the tedious task of manual tagging to save time and money
• Improved searchability for better user experience
• Personalized content based on user preferences.

Plus, AI-driven video indexing can identify specific objects, people, scenes, or spoken words within videos, allowing for precise and accurate search capabilities.

6. Real-Time Language Translation and Subtitling

One of the business goals of any profit-oriented streaming service is to reach as many users as possible. Localization allows video contents to be translated into different languages to reach a wider global audience.

For example, there are many text-to-speech AI models that generate voice-overs from subtitles and video scripts, enabling rapid content adaptation through AI content translation. Several streaming platforms, such as Netflix and Amazon, utilize AI video streaming models to automatically translate videos into different localizations for their user base.

How Cloudinary Simplifies AI-Driven Video Streaming

Cloudinary is a cloud-based media management platform that offers tools to help developers integrate AI video streaming workflows with ease. Here’s how:

Dynamic Media Delivery with Adaptive Bitrate Streaming

Cloudinary supports AI-driven adaptive bitrate streaming, delivering optimized video quality based on the viewer’s device and connection speed. In addition, Cloudinary supports both the HTTP Live Streaming (HLS) and Dynamic Adaptive Streaming over HTTP (MPEG-DASH) protocols. To deliver videos from Cloudinary using HLS or MPEG-DASH, you can either let Cloudinary automatically choose the best streaming profile, or manually select your own. You can learn more about using adaptive bitrate streaming with Cloudinary with their documentation.

Automated Format Conversion and Transcoding

As part of its Dynamic Video Platform solution, Cloudinary automatically identifies the end-user device and browser and then delivers the best video format and codec (any of H.264, HEVC, and VP9 codecs) for that user. This functionality eliminates the need for manual and complex optimization, making video delivery as pie.

Metadata Tagging and Search Optimization

Cloudinary provides support for the Amazon Rekognition and Immaga Auto Tagging add-ons which can autogenerate metadata tags and descriptions for videos, making them easier to organize and search. These tools use deep learning models to analyze the pixel content in videos, extract their features and detect objects of interest.

Real-Time Moderation and Content Control

Cloudinary offers robust content moderation tools powered by advanced AI, designed to automatically detect and flag inappropriate or sensitive material in both live and recorded videos. By analyzing visual and audio elements in real-time, these tools help to maintain a safe and inclusive streaming environment. With the Google Cloud Video Intelligence add-on, you can extend Cloudinary’s powerful cloud-based transformation and delivery capabilities with automatic and on-the-fly AI-based moderation for your videos.

Language Translation and Subtitle Integration

With the Google AI Video Transcription add-on, Cloudinary utilizes Google’s advanced neural networks to produce highly accurate video transcripts. This feature supports real-time subtitle generation and translation, enhancing accessibility and making your video content accessible to a global audience. You can read more about automatically generating subtitles with Cloudinary here.

Don’t Get Left Behind Without AI Video Streaming

Incorporating AI into video streaming opens up numerous possibilities, such as improved quality, personalized viewing experiences, accessibility, and content discovery. In this article, we highlighted six different ways you can leverage AI in your video streaming projects. Platforms like Cloudinary make it easy to integrate AI into video streaming, providing tools that optimize video performance, ensure compatibility, and expand reach.

Ready to take the next step towards optimizing your video streaming experience? Sign up for a free Cloudinary account today and take your projects to the next level!