Pre-caching Engine Schedule for Multicast Media Delivery
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Solution Overview
Problem
During busy traffic periods, mobile devices receiving streamed media content may experience data stream interruptions due to base stations being overwhelmed, impacting user experience as they cannot handle multiple requests simultaneously.
Innovation Solution
A pre-caching engine generates a schedule based on forecasted content requests and user device locations, allowing for pre-caching of media data at base stations and user devices, enabling multicast transmissions to multiple devices, thus reducing the load on base stations and preventing interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If base stations handle multiple content requests simultaneously during busy traffic periods, then user service coverage is improved, but system reliability deteriorates due to data stream interruptions
Solution Approach 1:
The system performs preliminary actions by predicting future content requests using machine learning models and pre-caching the predicted content at base stations before the actual requests arrive. This allows the base station to serve multiple users during busy periods without experiencing data stream interruptions, as the content is already available locally.
2Productivity
If base stations pre-cache content for multiple devices, then productivity is improved by reducing load, but device complexity increases due to scheduling requirements
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model continuously learns from actual user request patterns and adjusts predictions accordingly. The pre-caching schedule is optimized based on feedback from network traffic conditions, user behavior patterns, and cache hit rates, allowing the system to improve efficiency while managing complexity through adaptive learning.
3Loss of substance
If base stations use multicast transmission for pre-cached content, then loss of substance is reduced by single transmission to multiple devices, but device complexity increases due to coordination requirements
Solution Approach 1:
The system merges multiple unicast transmissions into a single multicast transmission for content that is predicted to be requested by multiple users. The machine learning model identifies content with high probability of being requested by multiple devices, and the system combines these requests into efficient multicast transmissions, reducing network bandwidth consumption while managing coordination complexity through intelligent content selection.
Data Source
AI summary
A method includes accessing information identifying user devices, and accessing media consumption data indicating content requests corresponding to the user devices. The method also includes determining expected locations of the user devices based on the information, and determining forecasted content requests based on the media consumption data. The method further includes generating a pre-caching schedule based on the forecasted content requests and the expected locations, and determining that the pre-caching schedule indicates that particular media data is to be provided via a multicast transmission to a first user device and to a second user device. The method also includes sending an instruction to the first user device directing the first user device to store the particular media data. The method includes sending, responsive to a request from the second user device, the particular media data via the multicast transmission to the first user device and to the second user device.