AI Video Content Selection for Live Event Streaming
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Solution Overview
Problem
Current multimedia streaming technologies fail to guarantee Quality of Service (QoS) and Quality of Experience (QoE) during live events due to network congestion, especially in high-bandwidth scenarios like sports and public events, where existing wireless broadband networks are overwhelmed, and private connectivity solutions lack sufficient bandwidth to transmit high-resolution video feeds.
Innovation Solution
A method and system utilizing AI models to analyze and optimize video content for transmission, selecting optimal video content and delivery channels based on user and sociological parameters, processing it into suitable streaming formats, and dynamically switching between OTT and broadcast channels to maintain QoS and QoE, leveraging a hybrid transmission service and unicast networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple video feeds from multiple cameras are transmitted to serve diverse user needs, then user coverage and service quality are improved, but network bandwidth consumption increases causing congestion
Solution Approach 1:
The system segments video feeds into multiple quality levels (e.g., 4K, FHD, HD) and transmits them through separate channels. AI models analyze user preferences and network conditions to assign appropriate video quality to different users, allowing the same event to be delivered to multiple users simultaneously without requiring all users to receive all video feeds at maximum quality.
Solution Approach 2:
Different users receive different video qualities based on their individual needs and network conditions. The system applies local quality optimization by matching video resolution and quality level to each user's subscription tier, device capabilities, and available bandwidth, rather than uniformly delivering the same quality to all users.
2Manufacturing precision
If high-resolution video feeds (4K, 360-degree) are transmitted to support AR/VR/use cases, then video quality and user experience are improved, but network bandwidth requirements increase causing uplink congestion
Solution Approach 1:
The system dynamically changes video parameters (resolution, frame rate, encoding bitrate) based on network conditions and user requirements. AI models predict optimal video parameters for each user based on their subscription level, device capabilities, and real-time network status, allowing quality adjustment without fixed bandwidth commitments.
Solution Approach 2:
Instead of transmitting all high-resolution video feeds to all users, the system provides only the necessary video quality level for each user's needs. Users with premium subscriptions receive higher quality feeds while basic users receive compressed versions, reducing overall bandwidth consumption while maintaining service quality for paying customers.
3Reliability
If AI-based optimal content selection and dynamic channel switching are implemented, then QoS and QoE are improved, but system complexity increases
Solution Approach 1:
AI models serve as intermediaries between video sources and users, automatically analyzing content, predicting user preferences, and selecting optimal delivery channels. The system uses AI-driven content selection and dynamic routing to make intelligent decisions about which videos to transmit and through which channels, reducing the need for manual configuration and complex network management.
Solution Approach 2:
The system implements feedback mechanisms where AI models continuously monitor network conditions, user behavior patterns, and video performance metrics. Based on this feedback, the system dynamically adjusts content selection and delivery channel assignment in real-time, improving QoS and QoE through adaptive optimization rather than static configuration.
Data Source
AI summary
Disclosed herein is a method and system for optimizing video content acquisition and delivery during events. In an embodiment, an optimal video content for transmission is selected by analyzing plurality of videos of a live event captured by one or more video capturing units. Further, optimal video content is transmitted to a Media Processing Center (MPC) using a predefined unicast network. Furthermore, the optimal video content is processed into one or more predefined streaming formats corresponding to one or more streaming channels of a hybrid transmission service. Subsequently, an optimal delivery channel for each of a plurality of users is selected by analyzing a plurality of user parameters associated with the plurality of users and a plurality of sociological parameters associated with the live event. Finally, the optimal video content is transmitted to the plurality of users through the optimal delivery channel selected for each of the plurality of users.