Edge Cache Segment Prefetching for Video Delivery
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Solution Overview
Problem
Current video delivery systems using edge servers face challenges in efficiently pre-loading and pre-fetching video chunks for adaptive bitrate streaming, leading to delays and buffering issues, especially for less popular or long tail video content.
Innovation Solution
The proposed solution involves a method and system for pre-loading and pre-fetching video chunks at an edge server, where a client device signals the edge server to preload video chunks for upcoming segments, and the edge server retrieves and stores these chunks ahead of time, reducing latency and buffering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video chunks are pre-loaded at edge server using traditional methods, then video delivery speed is improved, but storage capacity of edge server is exceeded and less popular video content cannot be cached
Solution Approach 1:
The system performs preliminary actions by pre-loading video chunks at the edge server before they are actually requested by clients. The manifest file is analyzed in advance to identify upcoming video segments, and corresponding video chunks are proactively fetched and cached at the edge server, enabling faster delivery when clients request them.
Solution Approach 2:
The video program is divided into multiple segments, and each segment is further divided into individual video chunks. The system selectively pre-loads only specific chunks from upcoming segments based on the manifest file analysis, rather than caching entire videos or uniform portions, thereby optimizing storage utilization.
2Reliability
If video chunks are pre-fetched for upcoming segments, then buffering issues are reduced, but network bandwidth is consumed before actual playback need
Solution Approach 1:
The system applies partial action by pre-fetching only a subset of video chunks from upcoming segments rather than all available chunks. It analyzes the manifest file to determine which specific chunks are most likely to be needed soon, fetching only those partial portions, thus reducing unnecessary bandwidth consumption while still improving buffering performance.
Solution Approach 2:
The system dynamically adjusts pre-fetching parameters such as the number of segments to pre-load, the bitrate variations to cache, and the timing of pre-fetch operations based on network conditions and client playback patterns, optimizing the balance between buffering reliability and bandwidth efficiency.
3Adaptability or versatility
If multiple bitrate versions of video chunks are cached, then adaptive bitrate streaming performance is improved, but storage requirements at edge server increase significantly
Solution Approach 1:
The system applies local quality by selectively caching multiple bitrate versions of video chunks only for specific upcoming segments that are most likely to be requested, rather than uniformly caching all bitrates for all segments. This allows adaptive bitrate streaming to function effectively for critical segments while conserving storage capacity.
Solution Approach 2:
The video content is segmented into multiple chunks across different time segments, and the system selectively pre-loads multiple bitrate versions only for chunks from upcoming segments based on manifest analysis. This segmentation approach enables versatile adaptive streaming where needed while minimizing storage requirements overall.
Data Source
AI summary
Devices, computer-readable media, and methods for pre-loading video chunks of a video program at an edge server are disclosed. For example, a processor may receive a manifest file for a video program, determine a bitrate for a first segment of the video program, request a video chunk of the first segment at the first bitrate from an edge server in accordance with the manifest file, and request a subset of a video chunk of a second segment of the video program from the edge server.