Streaming Data Transfer Optimization via Context Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Variable and unreliable data transfer rates during content delivery to user devices, such as mobile devices, can lead to performance degradation due to variable throughput or connectivity issues, affecting playback quality and continuity.
Innovation Solution
A method and system that predict the context and communication profile of a device, adjusting data transfer rates and playback rates to ensure content is transferred and played back smoothly, by mapping locations against connectivity information and adapting streaming or downloading strategies based on predicted connectivity changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data transfer rate is increased to improve playback quality, then playback quality is improved, but connectivity reliability deteriorates due to variable throughput and periods of no connectivity
Solution Approach 1:
The system predicts future connectivity conditions and pre-downloads content during periods of high connectivity before connectivity deteriorates. This allows the device to have content cached locally, ensuring continuous playback even when connectivity becomes unreliable or unavailable.
Solution Approach 2:
The system dynamically adjusts the data transfer rate based on predicted connectivity conditions. When poor connectivity is predicted, the system reduces the playback rate and adjusts the transfer rate accordingly, allowing flexible adaptation to varying connectivity conditions while maintaining continuous playback.
2Reliability
If data transfer rate is decreased to ensure continuous connectivity, then connectivity reliability is improved, but playback quality deteriorates
Solution Approach 1:
The system predicts future connectivity conditions and pre-downloads content during periods of high connectivity before connectivity deteriorates. This allows the device to have content cached locally, ensuring continuous playback even when connectivity becomes unreliable or unavailable.
Solution Approach 2:
The system dynamically adjusts the data transfer rate based on predicted connectivity conditions. When poor connectivity is predicted, the system reduces the playback rate and adjusts the transfer rate accordingly, allowing flexible adaptation to varying connectivity conditions while maintaining continuous playback.
3Productivity
If data transfer is planned based on predicted context, then data transfer efficiency is improved, but system complexity increases due to context prediction and communication profile determination
Solution Approach 1:
The system predicts future connectivity conditions and pre-downloads content during periods of high connectivity before connectivity deteriorates. This allows the device to have content cached locally, ensuring continuous playback even when connectivity becomes unreliable or unavailable.
Solution Approach 2:
The system automatically determines communication profiles and plans data transfers based on predicted context without requiring manual user input or complex external coordination. The device autonomously monitors connectivity, predicts future conditions, and adjusts its data transfer strategy accordingly.
Data Source
AI summary
The invention relates to a method of delivering content to a device, comprising: receiving a prediction of a context of the device; determining a communication profile for the device based on the predicted context; receiving an identification of content to be transferred to the device; and planning a data transfer to the device in dependence on the communication profile of the predicted context and the identified content. Adjusting data transfer in a vehicle based on download volume forecast.