Top OTT Platforms
OTT stands for over-the-top media that offers streaming services via the internet. It provides film and television content based on the demands of individual customers. OTT “over-the-top” signifies services that went “over” the heads of streaming services by transferring content over a high-speed internet connection rather than sharing the content with traditional distributors, which include broadcasters, distributors, IPTV, and cable operators. Some popular streaming platforms are Netflix, Hulu, Amazon’s Prime Video, HBO Max, Disney+, YouTube, Apple TV+, Peacock, Tubi, Quibi, etc.
Netflix
Netflix is one of the most popular OTT platforms in the world. A subscription-based streaming service that provides a platform for users to watch various movies, TV shows, anime, and documentaries across various genres and languages. It also offers Netflix for kids, which consists of kid-friendly TV shows and movies for all age groups. Users can access the content on internet-connected devices anytime, anywhere, and as much as they want. Recently Netflix launched mobile games for android users around the world.
Amazon Prime Video
Amazon Prime Video, or Prime Video, is a rental service by Amazon. It is an American subscription video-on-demand OTT service. Prime Video offers popular award-winning movies, TV shows, cult classics, newly released movies, sports, exclusive and live events, and Amazon Originals. It also offers prime features such as High Dynamic Range (HDR), 4K ultra-HD video quality, and mobile downloads to view the content offline. Prime video consists of more than 100 channels: BBC Select, A&E Crime Central, BBC Select, BET+, Boomerang, NBA TV, NBA League Pass, PBS Documentaries, PBS Kids, PBS Living, and more.
YouTube
YouTube is a free online video-sharing platform owned by Google where users can watch online videos and can also like, comment, share, and upload videos. It is the second most visited website after Google. More than one billion monthly users jointly watch more than one billion hours of videos each day. YouTube offers various services such as YouTube search (where the user can search anything and watch), news and information, video recommendations, health information, Live videos, and monetization for creators (the creators can earn money by uploading content, and advertising, subscriptions, and merchandise sales). YouTube has a premium service, YouTube Premium, where users can watch add free videos and download content to watch offline.
Hulu
Hulu is a subscription-based streaming service owned by The Walt Disney Company. It offers live and on-demand movies and TV shows with and without commercials. Hulu provides access to current shows to viewers from most U.S. broadcast networks. It also provides a library of hit films and Tv series and Hulu originals, Hulu plus Live TV (gives access to entertainment and sports TV, ABC, 80+ live news, Disney+, ESPN, NBCUniversal, CBS Corporation Discovery networks, and many more.
Market Share in the OTT Marketplace
Covid 19 pandemic has positively affected the OTT market. Due to the pandemic, people are consuming more content at home. The main reason for the growth of OTT is the high demand for unlimited access to high-quality OTT content. The global over-the-top (OTT) market size was valued at US$ 150.51 billion in 2021, and it is expected to hit around US$ 1241.6 billion by 2030, registering a CAGR of 26.42% from 2022 to 2030. OTT services are predicted to cover over 12 million new users in the US by 2026. The US is one of the biggest OTT markets in the world.
Impact of Covid-19 on OTT Platforms
OTT technology plays an important role in the growth of the media industry. Viewers can access their favorite shows and films on any device with the help of the internet. You can access the subscriptions from anywhere in your house by signing into your OTT platform account on different devices such as laptops, smartphones, smart TVs, desktops, gaming consoles, tablets, and set-top boxes.
Covid-led shutdowns forced everyone to stay inside their homes and look for new entertainment sources. This led immense boom in OTT platforms, and thus it became the main source for viewers in the Media and Entertainment world. COVID-19 led to various restrictions involving remote working, social distancing, closure of commercial activities, and others. The government across the world took measures to prevent and minimize the spread of the virus by shutting down various firms, schools, colleges, universities, cooperate organizations, cinemas, theatres, shopping malls, etc.
The global OTT streaming market has grown from $129.67 billion in 2021 to $149.34 billion in 2022 at a compound annual growth rate (CAGR) of 15.2%. As more and more customers are shifting from conventional subscriptions, i.e., from regular television to on-demand music and video services due to their ease of access, OTT streaming services are expected to grow rapidly.
Patents Filing Trends in OTT Market
Lumenci has analyzed the latest granted patents to top OTT players and provides insights into the future trends in the OTT domain.
Digital Navigation Menus to Customize the Content
The patent US11366872 filed by Amazon discloses that the OTT platforms may include digital navigation menus that contain dynamic content. People use the navigation menus of mobile applications or computer applications (such as desktops, computers, etc.) to navigate to the desired content or through different portions of the application. It includes links to content such as services or product information or information of content.
Based on the desired content, the user may select several options in order to be directed to the content. For example, the user may select a menu option and then a sub-menu option to reach the desired content. The content may be placed at or near the bottom of the menu list, which can be accessed by scrolling through the whole menu list. Therefore, the user may make several selections, such as scrolling, swiping, etc., to access the particular content. These navigation menus were static and included the same links or content until the application was updated.
Further, each user has different preferences, and the user wishes to quickly access the desired content from the main menu rather than a sub-menu. This involves a lot of scrolling and selections in using the static navigation menu. Therefore, the digital navigation menu provides the dynamic positioning of the links to content and customized or personalized options that users can select to access the desired content.
The contents in the navigation menu, such as images, links, swipeable cards, selectable options, and other content, may be placed dynamically or repositioned. The contents can be reordered or rearranged based on user preferences, their predicted selections, time of day, seasonality, geographic location, and other factors. For example, swipes, gestures, taps, user selections, audio commands, and the like may be observed and used to predict future selections. It may be used to determine the frequency of selection. This user interaction data may be used to determine the navigation menu’s arrangement of links or content.
The arrangement of links or content in the navigation menu can also be selected based on the selection rate or click-through rate (e.g., the number of clicks on a link divided by the number of impressions the link presents, etc.). Therefore, the navigation menu can be customized based on user selection or interaction. This reduces the amount of time spent scrolling the menu to find the link, access, or be directed to content. The user may locate or find the desired content with a minimum number of interactions, thus improving the user experience with the application. This way, time spent searching will be reduced, and new relevant content can be presented.
Interactive Interface for Identifying Defects
Video content is any content format (e.g., vlogs, live videos, recorded presentations, animated GIFs, etc.) that features or includes video. The demand for video content is increasing day by day. The video content is produced by video provisioning sources and is streamed to different user devices (such as laptops, televisions, smartphones, wearable devices, tablets, AR and VR devices, etc.) via the internet.
Every device receives video content at different resolutions and at different frame rates. It uses different software applications to decode the video content. Therefore, it becomes difficult to identify whether the video content will be displayed properly on every device. Some video content may have problems or defects that need to be removed. Some defects are visible only at high very high resolutions (e.g., ultra-high definition UHD or 4K), and some appear at low resolutions. The defects can be digital artifacts or dead pixels. Software algorithms are used to find the dead pixels or defects in video content, but the user must navigate the spot in the movie to identify the defect and verify whether the defect is present at that location.
Lumenci has observed a recent patent published by Netflix US11249626 in the year 2022. In this an interactive user interface is provided that allows users to control operations to navigate, identify, verify and remove the defects in the video content. It is based on the type of content, encoding, and other factors. It allows the user to easily navigate through a list of defects and or select individual defects. The user can also highlight the defects in particular locations on a frame. Once the defect is highlighted, a software algorithm may be used to determine the information about the defect. This determines a group of defects that is present in the movie or if it occurs repeatedly. For example, a defect may be present in a frame at a 4K resolution or at an HD resolution. Further, the user can select a defect, and the display will show the frame of the content having the defect. The user can also highlight the defects and perform other actions.
Use of AI and ML to Improve the User Experience
The OTT platforms are using artificial intelligence (AI) and machine learning (ML) technology to personalize the user experience. Users are more likely to watch the content based on their taste. OTT service providers use AI to customize the content based on the preferences of viewers and personal tastes. AI can determine this preference based on content rating, the shows you like and don’t like, and the amount of time spent on a particular show. OTT organizes the metadata into the content. Every video frame present in the OTT content library contains extra information (i.e., metadata) about the show such as the emotional state of the characters and the nature of their actions. AI helps to extract this metadata from videos and provides detailed descriptions. It automatically assigns labels to every scene. This metadata is used by OTT for various reasons such as content discovery, orchestration, and post-processing. It is an efficient method to enrich already present metadata or create new metadata by examining the video scenes and closed captions present within the content. For this OTT uses image recognition. Image recognition uses AI to classify people, places, objects, and actions in video or image scenes. Extracted metadata can be used by OTT services to build powerful user experiences, advanced search content discovery, and personalized recommendations.
Use of Machine learning in the OTT Platforms
The recent patents extracted by the author show the use of AI (Artificial Intelligence), ML (Machine Learning), and the Internet of Things (IoT) in the top OTT services to improve the user experience.
In the year 2022, Netflix published a patent US11284140 that determines the quality of user experience using machine learning techniques. The video streaming service provides access to various video content that can be watched on various client devices under different connection and network conditions. For efficient delivery of the video content, the streaming services first encode the video content and then streams the encoded video content to the client device. Based on the encoded video content, each device generates the reconstructed video content and displays reconstructed video content to users.
When viewing the reconstructed video content, there are variations in the quality of encoded video content which impact the quality of the user experience. Therefore, visual quality metrics are used to determine the quality of user experience by predicting the quality of encoded video content. But visual quality metrics have a drawback: they do not give back playback issues such as “rebuffering events” related to network throughput. In this case, the quality of the user experience is degraded, but the value of the visual quality metric remains the same. Various quality of experience (QoE) models is present that predict both the quality of encoded video content and the effect of any rebuffering events. However, the present QoE models lack robustness and cannot predict the quality of user experience in the case of a broad range of encoded video content and rebuffering events.
In this patent US11284140, Netflix determines the quality of user experience using machine learning techniques, an exponential QoE model is generated based on a training database, a visual quality metric, and a subjective database. The training database consists of training streams; each has video content with a certain playback duration. For every training stream, the QoE score is computed based on personalized QoE ratings when the user views the reconstructed content that includes any playback interruptions, such as any rebuffering events.
For every training stream, a value for the visual quality metric (that is a visual quality score) is obtained by obtaining a feature set for the encoded frames and a rebuffering duration. Next, the training engine then performs machine learning operations on a linear regression model (a linear form of the QoE model) based on the feature set to generate an exponential quality of experience (QoE) model that includes a QoE score. The QoE score measures the quality of user experience when viewing video content that has been encoded and streamed.
The model predicts the quality of the user experience. First, the model computes a first visual quality score based on streams of encoded video content. It then determines the first rebuffering duration and further computes an overall quality of experience (QoE) score based on the first visual quality score and the first rebuffering duration. An exponential QoE model is generated using machine learning operations and the overall QoE score indicates a quality level of a user experience. The exponential QoE model has a lower bound, is more intuitive and it provides more accurate results than the earlier QoE models.
As shown in the above figure, the QoE prediction subsystem consists of QoE model, and the training playback duration (i.e., the time required to playback the frames). The chunking engine generates the stream chunk based on the training playback duration, chunk overlap, and the target stream (video that has been encoded and streamed). For each stream chunk, the visual quality prediction subsystem determines the visual quality score based on the playback time of each stream chunk frame.
Further, the rebuffering analysis engine determines the rebuffering duration for each of the stream chunks. Then the QoE prediction engine computes the chunk QoE score based on the visual quality score, the rebuffering duration, and the exponential QoE model. Based on the chunk score for each target stream, the QoE aggregation system calculates the stream QoE score. The score predicts the quality of user experience (QoE) and it shows the impact of both reconstructed video content and the impact of rebuffering events.
Use of Internet of Things (IoT) based on User Interaction to personalize the OTT content
The author has pulled out a recent patent, US11109099 published in the year 2021 by Disney in the field of IoT (Internet of Things). The Internet of Things (IoT) defines the network of objects (i.e., things) that are embedded with software, technology, and sensors to connect and exchange data with other devices over the internet.
This technology will allow the streaming application to automatically personalize the playback of the media titles by user interaction based on the movement of an IoT device. The IoT device can be any physical object the user can interact with or move and communicate via the internet. It can be any object to interact with the media title, or that can be controlled by the user.
The streaming application causes the client device to playback the first chunk of a media title. During the play of the first chunk, the interactive streaming application determines the movement of an IoT device which is controlled by the user. Based on the first chunk and the movement of an IoT device, the interactive streaming application further performs machine learning methods such as reinforcement learning operations. This process determines the second chunk of the media title to playback. The application then playback the second chunk of the media title. Thus, the interactive streaming application can automatically personalize the playback of the media title for the user based on the movements of the IoT device.
As shown in the above figure, a personalized narrative is represented. It consists of playback chunks (i.e., 192 (1) – 192 (P), where P can be any positive integer). The interactive streaming application transmits the playback chunk to the client’s device. Further, the narrative personalization engine and/or the interactive streaming application determine a playback rate at which the new playback chunks are added to the narrative personalization engine. The narrative personalization engine uses reinforcement machine learning techniques to determine the playback chunks.
Based on iterative interactions between a user and the IoT device, it decides which of the chunks needs to be added to the personalized narrative. This improves the viewing experience of the user such as the user viewing the media title for a fixed amount of time (e.g., for at least thirty minutes).
Further, the user can control the IoT device for a specified interval or during the entire playback of the media title. The tracking stream tracks the interactions between user and the IoT device with the help of sensors such as biometric sensors, accelerometers, gyroscopes, GPS receivers, magnetometers, etc. During the playback of the media title, the movement recognition recognizes the movement of an IoT device, and then the personalization engine determines the playback chunks that can be overridden or refined by the overall playback system. Thus, the streaming application determines the second chunk of the media title to playback and automatically personalizes the playback of the media title based on the user interaction and movement of the IoT device.
Author
Editorial Team at Lumenci
Through Lumenci blogs and reports, we share important highlights from the latest technological advancements and provide an in-depth understanding of their Intellectual Property (IP). Our goal is to showcase the significance of IP in the ever-evolving world of technology.
