Intermediary Content Prefetching for Active Live Stream Users
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Solution Overview
Problem
Existing server-side ad insertion systems face challenges in handling millions of concurrent requests for intermediary content during live streaming events, leading to suboptimal user experiences and reduced advertising effectiveness due to limited processing time and resource constraints.
Innovation Solution
A content decision system utilizing a machine-learning model to predict user activity status based on user maps, enabling prefetching and caching of intermediary content, and transmitting it only to active users, thereby optimizing resource utilization and ensuring timely delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system processes all concurrent requests for intermediary content during live streaming events, then advertising coverage is maximized, but system resource exhaustion and processing delays occur
Solution Approach 1:
The system performs preliminary actions by receiving and processing requests to prefetch intermediary content before the actual ad insertion point is reached. The content is fetched, processed, and cached in advance during idle periods or low-traffic intervals, so that when the live streaming event occurs and multiple concurrent requests arrive, the system can immediately serve the pre-prepared content without real-time processing delays.
2Quantity of substance
If the system transmits intermediary content to all user devices, then advertising reach is maximized, but network bandwidth and server resources are wasted on inactive devices
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user device activity status through periodic signals (heartbeats) from client applications. This feedback information is used to dynamically update the user map, which tracks which devices are currently active and capable of receiving content. The system uses this feedback to intelligently route intermediary content only to active devices, avoiding wasteful transmissions to inactive devices while maintaining comprehensive advertising reach.
3Loss of time
If the system processes ad insertion in real-time during live streaming events, then ad freshness is maximized, but processing time and resource constraints reduce user experience quality
Solution Approach 1:
The system performs ad insertion processing in advance before the live streaming event reaches the ad insertion point. Requests are received and processed during idle periods or low-traffic intervals, allowing the system to prepare content bundles and update manifests beforehand. This eliminates real-time processing delays during critical viewing moments, ensuring both low latency and high user experience quality.
Solution Approach 2:
The system segments the ad insertion process into distinct phases: request reception and content fetching occur during idle periods, while manifest updates and content delivery are synchronized with the live streaming timeline. This segmentation allows resource-intensive operations to be performed when they won't impact user experience, separating background processing from foreground delivery.
Data Source
AI summary
A method of transmitting intermediary content to a user device may include receiving a periodic signal from the user device. The periodic signal may indicate an activity status of the user device. In response to receiving the periodic signal, the method may further include updating a user map with the activity status of the user device. The method may further include receiving a request to transmit intermediary content to the user device. In response to receiving the request to transmit intermediary content, the method may further include determining that the activity status of the user device in the user map is an active status. The method may further include transmitting a set of cached intermediary content to the user device.