Predictive Stream Caching for Live Video Delivery
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Solution Overview
Problem
Delivering live or linear streaming content over digital networks is challenging due to buffering and latency issues caused by overwhelming origin servers, and traditional caching methods are impractical due to large file sizes, leading to decreased cache-hit ratios and performance degradation.
Innovation Solution
Intelligent predictive stream caching, which involves prefetching and segmented caching, where distribution servers prefetch streaming content segments within a defined interval, cache them, and use request clustering to manage requests, reducing the load on origin servers and improving delivery efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If distribution servers cache entire streaming content files, then streaming content delivery speed is improved, but cache memory is overwhelmed and other content cannot be served
Solution Approach 1:
The patent segments the streaming content into discrete time-based segments (e.g., seconds or minutes of video) that can be independently cached and served. This allows the distribution server to cache only the portions of content that are actually requested by users, rather than caching entire large files, thereby reducing memory pressure while maintaining fast delivery for popular segments.
Solution Approach 2:
The patent implements partial caching by storing only a subset of the streaming content (specifically, segments that are predicted to be requested or are currently requested) rather than caching the complete content. This partial action approach allows the server to serve content quickly when needed without consuming excessive cache memory on content that may never be viewed.
2Quantity of substance
If distribution servers retrieve fresh copies of content from origin servers for each request, then cache memory is preserved for other content, but delivery time increases and buffering occurs
Solution Approach 1:
The patent employs predictive algorithms that analyze user viewing patterns, content popularity, and temporal data to predict which content segments are likely to be requested in the future. The distribution server proactively retrieves and caches these predicted segments before users actually request them, eliminating delivery delays while ensuring cache memory is allocated efficiently based on predicted demand rather than static allocation.
Solution Approach 2:
The system continuously monitors actual user requests and compares them against predictions, using this feedback to refine the predictive algorithms. This feedback loop allows the cache to learn which content segments are most valuable to store, improving both prediction accuracy and cache utilization over time, thereby reducing unnecessary content retrieval delays.
3Reliability
If a deep distribution server hierarchy is used, then load on origin servers is reduced, but latency increases and more buffering occurs
Solution Approach 1:
The patent enables distribution servers to perform preliminary content retrieval and caching based on predictive algorithms, allowing them to stock up on popular content segments in advance. This preliminary action reduces the need for deep hierarchical distribution chains, as closer-to-user servers can serve content from their local caches rather than relying on distant origin servers, thereby reducing latency while still managing origin server load effectively.
Data Source
AI summary
Some embodiments provide intelligent predictive stream caching for live, linear, or video-on-demand streaming content using prefetching, segmented caching, and request clustering. Prefetching involves retrieving streaming content segments from an origin server prior to the segments being requested by users. Prefetching live or linear streaming content segments involves continually reissuing requests to the origin until the segments are obtained or a preset retry duration is completed. Prefetching is initiated in response to a first request for a segment falling within a particular interval. Request clustering commences thereafter. Subsequent requests are queued until the segments are retrieved. Segmented caching involves caching segments for one particular interval. Segments falling within a next interval are not prefetched until a first request for one such segment in the next interval is received. Cached segments from the previous interval can be replaced in cache with segments for the current interval, thereby minimizing cache footprint utilization.