Reference Frame Caching for Low-Bandwidth Video Streaming
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Solution Overview
Problem
Streaming video content places high demands on bandwidth due to inconsistent connection quality and traditional video compression solutions often provide marginal savings at the expense of video quality.
Innovation Solution
Implementing a system that assembles a set of reference frames, using neural networks to identify and cache frames with significant attribute variations, and transmits them as one-time transfers, while using adaptive blur detection to manage bandwidth efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional video compression solutions are employed to reduce bandwidth requirements, then bandwidth consumption is reduced, but video quality deteriorates
Solution Approach 1:
The patent segments the video stream into key frames (I-frames) and non-key frames (P-frames), transmitting only the differences between frames rather than entire frames. This segmentation allows bandwidth reduction by sending only essential change information while maintaining acceptable video quality through intelligent frame selection and differential encoding.
Solution Approach 2:
The system performs preliminary actions by caching reference frames and pre-computing motion vectors before actual transmission. By preparing and storing reference frames locally, the system can efficiently transmit only the differences during streaming, reducing real-time bandwidth requirements while maintaining quality through pre-established reference points.
2Adaptability or versatility
If video streaming is implemented to enable remote work interaction, then workplace connectivity is improved, but network bandwidth requirements increase
Solution Approach 1:
The patent implements dynamic frame selection that adapts to network conditions and video content. The system dynamically determines which frames to transmit based on motion detection and scene analysis, adjusting the transmission strategy in real-time to balance remote connectivity requirements with available network bandwidth, allowing flexible work interaction without fixed bandwidth demands.
Solution Approach 2:
The system extracts and transmits only the essential information by removing redundant data. Through motion compensation and difference encoding, it extracts only the necessary change information between frames rather than transmitting complete frame data, enabling remote work connectivity while significantly reducing network bandwidth consumption.
3Loss of energy
If reference frames are cached and transmitted as one-time transfers, then bandwidth requirements are reduced, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting motion, identifying key frames, and assembling reference frames without external intervention. The frame assembly mechanism autonomously manages caching, selection, and transmission based on built-in motion detection algorithms, reducing bandwidth requirements while keeping device complexity manageable through self-managed operations.
Solution Approach 2:
The patent introduces an intermediary frame assembly mechanism that mediates between the video source and transmission channel. This intermediary layer handles the complexity of frame selection, caching, and optimization, isolating the complexity from the core transmission system and enabling bandwidth reduction through managed frame handling rather than direct complex processing.
Data Source
AI summary
Systems and methods herein address reference frame selection in video streaming applications using one or more processing units to replace, during receipt of an encoded video stream, a first set of frames stored in a cache with a second set of frames based at least in part on an indication within the encoded video stream that the second set of frames includes a non-blurred frame (NBF).