How Is Machine Learning Applied To Online Video Streaming?

Technological advances have left a deep impact on almost every aspect of our lives. In some areas, the influence of these advances is more profound than others. Implementation of these changes in them has resulted in superlative output, thereby redefining its core concept.

Applying machine learning to online video streaming solutions is a perfect example of the same. Today safety and security concerns regarding video websites have reduced substantially and new content is readily available to users. It would not be wrong to say that machine learning algorithms have reshaped the core concept of online video streaming. Thus streamed online videos benefit immensely from the same.

What is Machine Learning (ML)?

Machine learning is aptly defined as that subset of Artificial Intelligence which has empowered computers with the ability to learn. Today using statistical techniques computers can:

  • Identify tasks generally done by humans,
  • Learn to do them properly and
  • Improve over time.

Applicability of Machine Learning to Video Streaming

There is no dearth of ML applicability opportunities in the field of online streaming. Irrespective of the online video streaming platform used, all online video streaming solutions face certain common problems like:

  • Viewing or browsing behavior is different for different devices used by viewers,
  • Cellular networks tend to be unstable and comparatively more volatile than fixed broadband networks,
  • Some areas experience excessive congestion in their networks,
  • Hardware differences account for the variations experienced in internet connection capabilities and fidelities,
  • Providing quality streaming to a global audience,
  • Being able to cater successfully to the diverse behavior of the audience,
  • Having no control over the content that is being streamed,
  • Online streaming has become increasingly accessible,
  • Privacy invasion and setting of boundaries for online streaming are virtually impossible etc.

There is an urgent need for one solution that will be able to deal with and solve the above challenges that negatively affect the output of the online video streaming solutions.

The application of ML to online video streaming has shown considerable promise in the elimination of these challenges. Some was where ML can positively impact online video streaming are:

  • Object detection: This is a very practical usage of ML. Content regulation can be easily done using this process of tech-enabled censorship. While previously, manual monitoring was the norm, this has often resulted in violent and disturbing video streams becoming viral.  With the use of ML, even if such disturbing content is streamed on any online video streaming platform, the platform can act quickly, smartly, and much more effectively, to remove the content.

Object detection can be easily made by extracting metadata from the content. This will enable online video streaming platforms like YouTube, Facebook, Google, etc., to efficiently monitor the content that is uploaded. Harmful content can be discarded and the privacy of the victims of these harmful videos can be protected. This will make online video streaming solutions safer than before.

Also Read: Top 10 Live Streaming Platforms for Businesses

  • Content indexing: The number of online videos getting created is humongous. Thus there is an urgent need to catalog these streams. Machine learning-induced content indexing along with its ability for content personalization enables all content streamed online to get cataloged.  The advantage is that now users can content that interests them personally. Thus content indexing helps enhance user experience.
  • Prevention of copyright infringement: Providing copyright protection to live streams or content is a risky affair. But the implementation of ML has been able to prevent copyright infringements with its inherent content indexing and ability to flag illicit content. There is also the availability of video tools, mostly learning-based, and that can be used to analyze streaming live videos to check for the possibility of copywriting issues.
  • Content-Aware encoding: The proper format for encoding can also be easily determined using ML. Based on the complexity of the video, machine learning helps to optimize resources and enhance video quality. When low-bitrate online video streaming solutions are encoded, both the cost and the bandwidth can be saved.
  • Automation: ML enables automation and mission-critical workflows are automatically created to significantly improve user experience. The ML model also enables the media provider to automatically ensure delivery of personalized offerings by way of viewer relevant content, different subscription types, if the platform is a paid one, and payment methods.

Online Video Streaming Platforms using Machine Learning

The implementation of machine learning for the betterment of online video streaming solutions has already started. Some common and popular websites are already implementing machine learning tools for the better quality of online video streaming. These platforms include:

  • YouTube: Several processes are getting automated by making use of machine learning. From pairing advertisers with channels, deciding the content appropriateness based on viewing age, flagging violent and extreme content, etc.; YouTube has successfully implemented machine learning for all of this and much more.
  • Twitch: This is a platform for streaming online live streaming videos. The use of machine learning helps measure custom emotes to find out what is being streamed live online and engaging users with content creators.
  • Facebook: ML is used across all its streaming videos to improve user experience by removing spam content and build the ability for Facebook to read and interpret pictures and videos and translate around 2 billion stories daily.

The concept of the application of machine learning to online video streaming has a long way to go. What is known today is just the tip of the iceberg. Developers have only managed to scrape the surface with learning how to encode online videos,  index it and censor it for the global audience. Thus there is ample scope for the implementation of the power of ML to online video streaming solutions

Machine learning has helped Phando in enriching the viewer experience. Our continuous efforts towards incorporating the best of machine learning will ensure making moments memorable for end users.


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