Dynamic Media Caching for Wireless Network Optimization
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Solution Overview
Problem
Existing caching techniques, such as prefetching, consume significant resources and waste capacity when caching media files in advance of access, especially in wireless access networks, due to inaccurate predictions and high resource consumption before actual access.
Innovation Solution
A caching server dynamically selects and caches a portion of a media file in a User Equipment (UE) based on radio network conditions and media file characteristics, optimizing the cached size to match available resources and predicted user interest, thereby reducing unnecessary resource usage and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If prefetching is used to cache content in advance, then access time is improved, but resource consumption and capacity waste increase
Solution Approach 1:
The patent implements dynamic caching where the system continuously monitors network conditions, user behavior patterns, and content characteristics to adjust caching decisions in real-time. This allows the system to optimize between prefetching (when conditions favor it) and on-demand loading (when resources are constrained), resolving the contradiction between access time improvement and resource consumption.
Solution Approach 2:
The system changes key parameters such as cache size, prefetching aggressiveness, and caching priority based on network bandwidth availability, device storage capacity, and user engagement patterns. By dynamically adjusting these parameters, the system improves access time when resources permit while minimizing resource waste when constraints exist.
2Loss of time
If prefetching is used to cache content in advance, then access time is improved, but capacity waste increases
Solution Approach 1:
The patent implements partial prefetching where only portions of content are cached in advance based on predicted user interest and network conditions, rather than caching entire files. This selective approach reduces capacity waste while still providing the access time benefits for the most relevant content segments.
Solution Approach 2:
The system uses machine learning models that analyze user behavior patterns to self-determine what content to prefetch and in what quantities. This intelligent self-service approach ensures caching capacity is allocated to content most likely to be accessed, minimizing waste while maximizing access time improvements.
3Reliability
If initial buffering is performed before playback, then continuous media experience is achieved, but waiting time increases
Solution Approach 1:
The patent performs preliminary buffering of content based on predicted user viewing patterns and network conditions before the user actually requests playback. By anticipating user needs and pre-loading content during idle network periods or low-usage times, the system reduces perceived waiting time while ensuring continuous playback experience when the user does watch.
Solution Approach 2:
The system implements feedback mechanisms that monitor actual user viewing behavior against predictions, continuously refining the buffering strategy. This allows the system to learn from past behavior and optimize the balance between preliminary buffering (for reliability) and waiting time reduction, adjusting buffer sizes and prefetching aggressiveness based on real-world performance.
Data Source
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AI summary
The present invention concerns a method and an apparatus for enabling caching of a portion of a media file in a User Equipment, UE. A caching server (120) selects (610) the media file of which the portion is to be cached in the UE. The caching server determines (620) a size of the portion of the media file to be cached. The size is determined depending on radio network conditions for the UE and/or characteristics of the media file. The caching server sends (630) an instruction to the UE to cache the determined size of the portion of the media file in the UE.