Technologies powered by Artificial Intelligence are being introduced in almost every domain. From AI-based eCommerce stores to smart home assistants like Alexa and Google home, the plan is to smartly automate all that human can do.
Automation is not new to humanity. We have been programming computers and software to perform specific tasks for a long time. The difference that comes with AI in those machines is now they can perform much more than what they have been programmed for.
Simply, they can learn new things and take non-programmed decisions by mimicking human intelligence. A simple example to differentiate an AI-based machine could be seen in event-driven chatbots and AI chatbots.
Where event-driven chatbots can answer only those queries which have been programmed, AI chatbots can utilize Machine learning and Natural Language Processing to give a smart answer to even those queries which has not been programmed in them.
Here in this article, we have focused our discussion on the application of AI in video streaming services. By the end of this article, you would be able to comprehend a complex structure of AI-based optimization on how to stop video buffering problems in streaming services and ways to offer an optimized user experience.
Using Deep Neural Networks in Netflix’s video recommendations algorithm, the video streaming service can learn complex browsing patterns of the users on all the supported platforms and provide cognitive video suggestions for further browsing.
Both YouTube and Netflix use a high-level AI to generate video previews and highlights for two different reasons. Netflix generates dynamic video previews of selected videos on its Smart TV applications and WebRTC platform. Youtube allows live streamers to automatically generate highlights of the whole live sessions. You can create a shorter, edited version of your live stream session right after it’s over.
Both YouTube and Netflix utilize AI-based content tagging based on the manual tags provided. The feature helps the platform to group related content and offer more personalized video recommendations in addition to better search results for a performed search query.
Netflix uses machine learning to analyze video browsing behavior of the users. Based on different activities the platform can automatically trigger behavior-based push notifications. For example, it can remind a user to finish a movie that has been started but left in the middle.
To make the video content accessible to all viewers, YouTube uses AI-based Natural language processing to recognize speech and automatically create live captions on the videos.
YouTube has embedded its AI-based chatbot “Nightbot’ in live streaming chats. Nightbot allows streamers to automate live stream’s chat with AI-based moderation.
In an era of 4K video streaming, if some video streaming services are still stuck with problems of video buffering, survival in the market is definitely questionable for them. With a boom in the accessibility to high-speed internet and high-end smartphones, online video consumption has grown exponentially. However, video buffering can destroy the whole experience and force users to find some alternative.
In simple terms, video buffering is the time that online videos take to play after a playback is requested. The buffering can take place before the playback or even in-between the playback.
Buffering occurs for several reasons. However, a perfect condition for videos to start buffering is when the streaming device, for any random reason, is not able to get enough data from the streaming server to build a playback.
The failure in data collection from the server could happen because of any random reason such as –
In this era of AI-based auto-driving cars, a complete automation of video streaming process is not a vast task. In fact, researchers have found many ways to curb the above causes of buffering and ensure seamless video streaming. Filmmakers spend hours and many dollars on high end camera equipment. But that won’t make a difference, if the end user can’t watch the videos in high quality.
With AI and machine learning, video streaming networks can dynamically calculate the requirements of time, bandwidth, server consumption, and video formats for a buffer-free streaming on an array of devices, networks, and geo-location.
There are two ways to avoid heavy loads on servers and stop video buffering while streaming. One is getting a huge scale dedicated server and second is hosting on a CDN powered with scalable cloud servers.
With fixed large-scale servers, you might get an uninterrupted streaming capability during busy hours. However, the cost of renting such servers is really high. In fact, it is impractical for small and medium-sized businesses. You would not require large-scale servers everything; only except for the peak times. So why would you pay the huge cost even during the times you are not using the servers in full-scale.
On the other hand, there are AI-based cloud servers from providers like Microsoft Azure, Amazon cloud, and Google. These servers allow dynamic upscaling and down-scaling of server consumption as per the requirements. They are affordable, as they charge only for the consumption you make during the whole process.
Using AI, the servers predict the increase in resource requirements, and upscale or downscale the resources both vertically and horizontally to reduce the delay in response time. The process takes place something like in this sequence –
Condition 1– Normal Server consumption
Condition 2 – Increased requests due to heavy load
Condition 3 – Server re-allocated resources as per new requirements.
Condition 4 – Server back to normal consumption after peak time
Online videos are often encoded in multiple formats and resolutions to allow buffer-less streaming in multiple bandwidths and across devices. Users get the option to select preferred streaming quality and video is rendered to their devices in the requested formats. However, often times, users select HD qualities. As a result, if a user is not getting enough bandwidth, the video will start buffering to retrieve required data packets for resuming the playback.
To tackle this, streaming engines utilize AI for dynamically predicting the bandwidth, device compatibility, and other congestions, before buffering the exact amount of required data. This whole process can coordinate by a streamlined communication: Streaming application can be configured for aggressive high-quality videos, but it will increase the chance of re-buffering. Alternatively, the application can be set up for more downloads up front and reduce the rebuffer chance at the cost of increased waiting time before video playback starts.
AI in streaming applications is responsible for selecting the above conditions smartly as per the requirements and preferences of the users. However, it is a challenge for AI to learn all the optimal controls algorithms and utilize machine learning to predict which algorithm is best to stop buffering at a given condition.
This is how dynamic bitrate switching works on an MS Azure powered streaming server-
AI-based video caching can helps streaming application to stop video buffering by smartly predicting the content most likely to be played by a user in a certain time frame. Using the predictions, the application can buffer a significant amount of data before a user clicks on the video. In this way, the user will get an instant playback with advanced caching of video beforehand.
The AI in such applications is responsible for accurate predictions based on deep learning and smart allocation management of cache with dynamic pre-buffering and clearing of used video caches.
Video cache is managed by the streaming engine where it collects the cache and stores on the data center to instantly feed the video on request.
One more way to stop video buffering is to smartly select the routes for packet delivery. The packet delivery is however controlled by following different IP protocols in a given scenario.
AI can dynamically optimize the protocol selection to compress codecs and optimize network data packet transfers in a given bandwidth or server load.
Additionally, AI can also use complex mathematical calculations to smartly choose the over-the-top (OTT) delivery route by avoiding the congested and unstable routes to deliver the data packets on the streaming device.
AI uses machine learning to learn new ways to do the same task. With time, AI-based approaches get more accurate and perform tasks without error. AI at the different levels of streaming services is still a new concept.
Many have already adopted the smart capabilities while others are in transition. If you are still wondering how to stop video buffering when streaming, just understand that an optimized resource allocation is the only approach to do it, and that’s what AI does by utilizing only required amount of resources for a particular playback.
Resources could be anything from server consumption, requested bit-rate, supported video resolution, and required internet bandwidth to appropriate IP protocol. Ultimately, a streaming business can only succeed if is able to offer buffer-less streaming with highest possible video quality in a given condition.
About the Author
Amanda Smith is a marketing professional with expertise in strategies to engage customers and improve business opportunities. Follow her at:
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