AI algorithms can be used to automatically analyze the content and streamline certain processes in the flow of a video stream. Content personalization, automatic playlist creation, piracy reduction and rich metadata creation are just some of the other benefits that AI utilization can deliver.

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In streaming video, artificial intelligence (AI) can be used to personalize content, providing users with content and advertising tailored to their preferences and online behavior. AI algorithms can almost perfectly analyze and collect data about our habits and choices, when using streaming video platforms. They can examine likes, clicks and audience research, linear TV, and on-demand streaming platforms to collect information on a wide range of audience trends. This is now being done by AI and one of its subsets, machine learning. On streaming platforms in particular, AI algorithms are already analyzing data on user preferences, content searches, viewing history, interactions, and ratings. The target groups that can be built up in this way are able to be much more precise and more deeply fragmented than those which have been created to date.

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The use of artificial intelligence in video streaming platforms dates back to the days of the integration of content algorithms with the aim of maintaining viewer engagement. The launch of Chat GPT, which generates text from user suggestions, has triggered huge interest in artificial intelligence and its capabilities. Within five days of its launch (i.e. 30 November 2022), one million users were using Chat GPT, with the tool becoming an unquestionable hit on a global scale. Along with almost every other industry, the video streaming sector is now implementing AI in the technologies which are used on platforms. 

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In a highly competitive market, AI tools are helping streaming platforms to stand out. Artificial intelligence can manage many of the processes involved in video streaming, including clip selection, editing and recommended content selection. In the not too distant future, thanks to the wider use of AI, we will be able to completely change the approach to static or quantitative quality-performance configurations. This new insight is allowing us to slowly move away from the typical streaming build model. As a result of studying correlations and analyzing output, the transcoding (stream preparation) parameters of video streams based on Large Language Models (LLMs), can be reconfigured by artificial intelligence. AI can therefore automatically verify the status in real time by checking the parameters of the input stream and provide feedback to optimize the parameters of the output video stream. 

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Research into deep learning algorithms has shown tremendous progress in restoration, resolution enhancement, as well as image and video manipulation. Breakthroughs in deep learning algorithms have been made by generative AI models such as Google's Bard and GPT's OpenAI. The generative AI model, which has recently gained popularity, allows for the creation of text, code, images, simulations, audio, and video recordings by predicting the next word, sound, or pixel. Also being based on large language models (LLMs), generative AI is characterized by its ability to perform tasks that are not included in training cases. This unique advance in the development of systems to improve the understanding and generation of human language, together with the latest developments in multimedia content creation, could completely revolutionize our approach to content creation. Generative Artificial Intelligence tools are fast becoming indispensable for multimedia developers and service providers. The entire media and entertainment (M&E) industry will be able to create content more efficiently, more quickly, more ergonomically and, above all, more cost-effective by fully exploiting the correlation analysis, behavior, and expectations of their audiences.

The increased demand and supply of video content on streaming platforms and, incidentally, the use of short video ads on social media (as a marketing strategy) are driving the development of AI in the video production industry.

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Here's a brief overview of the key benefits that video streaming providers will be able to reap from the implementation of AI.

• An improvement in the quality of the video streaming - with AI-based encoding, providers can optimize settings in real time based on the type of content, improve compression performance and reduce buffering of the uploaded stream. AI-based encoding methods reduce the cost of video streaming and significantly improve the quality of the output video stream.
• Content personalization - the utilization of AI-based content analysis mechanisms allows providers to explore and identify similarities between different types of video content. As a result, the viewer receives more personalized recommendations of content with the provider seeing increased viewer satisfaction.
• Automated analytics - Artificial intelligence algorithms automatically analyze content, improve viewer discovery, and optimize video streaming mechanisms. Automatic AI analytics generate descriptions, metadata, tags, cast and crew information, and genre classifications for content providers and streaming platforms. These analytics also automatically create playlists based on the subject matter of the selected content and viewer demographics.
• Content localization - increasing the provider's reach, which is undoubtedly key in reaching viewers around the world with video content. By using AI technology, content providers can streamline and accelerate the process of capturing, translating on the fly as well as subtitling which are based on the viewers’ language preferences.
• Fighting piracy and securing video content - for example with digital watermarks using AI mechanisms - is another benefit for video providers. Thanks to AI, providers can instantly identify pirated video streams by capturing digital watermarks of pirated content. In this way, providers can protect their revenue streams.

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When optimizing artificial intelligence mechanisms in the video streaming ecosystem, it is important to decide when and at what stage of video streaming to implement AI. There are many ways in which artificial intelligence can be implemented: 

• in content-aware encoding, where AI recognizes the type of content being streamed and optimizes the bit rate, the delay, and the protocol;
• in advanced content-aware encoding technology that enables high compression, resulting in lower storage and CDN requirements for service providers (reducing costs);
• after an analysis, AI can be programmed to extract clips from a longer streamed video and select meaningful snippets to drive viewer engagement,
• in automating the segmentation of video-on-demand (VOD) content, enabling providers to individually target and quickly identify the most relevant advertising space, such as product placement in the video content, without disrupting the viewer's smooth reception of the video stream;
• in improving real-time video compression that allows VOD channels to be streamed at much lower bit rates, ensuring the best possible video stream quality.

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Video streaming based on the use of artificial intelligence mechanisms is very much the future of our industry. With AI-based technology, service providers can improve the quality of video streaming services, personalize content for viewers and increase viewer engagement. All of this will significantly increase video service providers' revenues generated from their streaming services. Challenges related to the security of personal data processed by AI and the increasing automation of many processes, which could lead to the elimination of jobs, are arguably the most significant risks that have appeared due to the widespread usage of artificial intelligence.

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