Content-Adaptive Video Ladders for Rebuffering Control
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Solution Overview
Problem
Conventional video streaming systems face challenges in optimizing video ladders for different network conditions and video complexity, leading to inconsistent playback quality and increased rebuffering events.
Innovation Solution
A content adaptive video ladder optimization technique that uses dynamic programming to generate video ladders tailored to individual user devices based on video complexity and network conditions, balancing video quality and playback characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed set of video ladders is used for all videos, then the system complexity is reduced, but the user experience and playback quality cannot be optimized for different video complexities and network conditions
Solution Approach 1:
The patent implements dynamic video ladder generation where the set of video ladders is automatically adjusted based on video complexity metrics and network conditions. Instead of using a fixed ladder set, the system dynamically determines optimal ladders for each video, allowing adaptation to varying content characteristics and user network environments while managing system complexity through automated decision-making.
Solution Approach 2:
The system changes parameters such as video resolution, bitrate, and encoding characteristics based on analyzed video complexity and network conditions. By varying these parameters dynamically, the system optimizes playback quality and reduces rebuffering events without requiring manual configuration, thus improving adaptability while controlling complexity through parameter-driven automation.
2Reliability
If video ladders are optimized for each video based on complexity and network conditions, then user experience and playback quality improve, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary analysis of video complexity metrics and network conditions before finalizing the video ladder selection. By preparing and analyzing video characteristics in advance, the system can pre-determine optimal ladders, reducing computational burden during actual playback and improving reliability without excessive real-time processing complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where playback performance data is collected and used to refine future ladder selections. This feedback loop allows the system to learn from actual user experience and adjust its optimization algorithms, improving reliability over time while managing computational complexity through iterative refinement rather than exhaustive analysis.
3Manufacturing precision
If higher video resolutions are transmitted to ensure quality, then video clarity improves, but network bandwidth requirements and rebuffering events increase
Solution Approach 1:
The system applies different quality levels to different parts of the video transmission based on local network conditions and video complexity. Instead of uniformly transmitting high resolution, the system selectively optimizes ladders for specific segments or overall video content, ensuring adequate quality where network conditions permit while reducing bandwidth consumption and preventing rebuffering in constrained environments.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for video streaming. One of the methods includes obtaining a collection of one or more video files at a content delivery system; for each video file: determining a set of video ladders for the video file, each video ladder corresponding to a transcoding version of the video file having particular parameters, the set of video ladders determined based on a set of individual ladder values that substantially maximize a measure of utility for one or more users viewing a content the video, wherein the utility of the video is based at least in part on a complexity of the video content and historical network characteristics; for each ladder in the set of video ladders, transcoding the video file into a corresponding version; and storing each transcoded version of the video for delivery to user devices.