Predictive Pre-buffering for Streaming Playback Latency
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Solution Overview
Problem
Users of electronic devices face delays in streaming content playback due to limitations in network bandwidth and processing power, leading to an unsatisfactory user experience when waiting for content to download before playback can begin.
Innovation Solution
A predictive pre-buffering system that analyzes user viewing history and trending content from social media sources to proactively buffer likely-to-be-watched programs in a device's cache, ensuring immediate playback when selected, by considering device and network availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the system buffers content in advance to enable immediate playback, then playback speed and user satisfaction improve, but network bandwidth consumption increases
Solution Approach 1:
The system performs preliminary buffering of content data in advance of user requests by monitoring trending topics and predicting user viewing preferences. This allows content to be pre-loaded into cache memory during off-peak hours or periods of lower network demand, enabling immediate playback when users access the content without requiring real-time bandwidth allocation.
Solution Approach 2:
The buffering system dynamically adjusts its behavior based on real-time network conditions, user preferences, and trending data. The system monitors network bandwidth availability and adapts the buffering rate and amount accordingly, optimizing the balance between enabling fast playback and conserving network resources during periods of constrained bandwidth.
2Loss of time
If the system pre-loads content into cache to reduce wait times, then user satisfaction improves, but device storage requirements increase
Solution Approach 1:
The system prioritizes buffering content based on predicted user preferences and trending data, allocating cache storage to specific high-probability content rather than uniformly distributing storage across all available content. This selective buffering approach ensures that the most likely-to-be-viewed content is readily available while minimizing unnecessary storage consumption.
3Productivity
If the system monitors network load and device status to optimize buffering, then buffering efficiency improves, but system complexity increases
Solution Approach 1:
The system implements self-service monitoring mechanisms that automatically track network load conditions and device status without requiring external intervention. The buffering system autonomously adjusts its operation based on real-time feedback from network and device sensors, optimizing buffering efficiency while minimizing the need for complex external control systems.
Data Source
AI summary
Described herein are systems and methods for predictive pre-buffering of content for a user. Embodiments include a prediction system that can receive trending information from social media sources and other streaming video sources. The prediction system can also receive user viewing history to identify the programs the user has viewed, the genres of the programs the user has viewed, and amount of programming the user consumes. Based on the user's viewing history, the prediction system can identify preferred genres for the user and other preferred content information. Based on the trending programs and the user's preferred genres, the prediction system can predict which of the trending programs that the user may watch. Based on the prediction, the system can pre-buffer some portion of the programs that the system predicted the user may watch in a cache of the device on which the user watches programming.