Compact Cumulative Bit Curves for Video Streaming
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Solution Overview
Problem
Existing video streaming technologies face challenges in delivering variable bit rate (VBR) encoded video content over networks without causing buffer underflow, which leads to interruptions, due to the high variability in bit rates, especially during high-action sequences, and require significant network overhead to transfer cumulative bit curve data for accurate rate calculations.
Innovation Solution
A compact representation of the cumulative bit curve is created using piecewise straight line approximations with upper and lower bounds, ensuring that any calculated data rate is at least as high as the actual rate, using algorithms like linear interpolation to minimize the number of endpoints, and selecting a coefficient (1+K) to guarantee sufficient delivery rates, where K > 0.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If variable bit rate (VBR) encoded video is delivered over the network, then video quality is improved, but buffer underflow occurs causing interruptions
Solution Approach 1:
The system performs preliminary calculations of forward delivery rates using the compact cumulative bit curve representation before actual video delivery. This allows the network to pre-determine appropriate delivery rates that prevent buffer underflow while maintaining high video quality, resolving the contradiction between quality and reliability.
2Measurement precision
If complete cumulative bit curve data is transferred across the network, then accurate rate calculation is achieved, but network overhead and time delay increase
Solution Approach 1:
The invention extracts only the essential characteristics of the cumulative bit curve by creating a compact representation using piecewise straight line approximations with bounds. This extracted compact form retains sufficient information for accurate forward delivery rate calculation while dramatically reducing the amount of data that needs to be transferred across the network, resolving the contradiction between accuracy and data volume.
Solution Approach 2:
The system transforms the complete cumulative bit curve data into a compact representation by changing the parameters from individual bit counts at each GoP to simplified bounds (upper and lower) and piecewise linear approximations. This parameter transformation maintains calculation accuracy while reducing data volume for network transmission.
3Ease of operation
If constant bit rate (CBR) encoding is used, then network delivery is simplified, but video quality varies over time
Solution Approach 1:
The invention enables dynamic delivery rate adjustment by providing the video client with compact cumulative bit curve data that allows real-time calculation of appropriate delivery rates based on current network conditions and video content characteristics. This dynamic approach maintains constant video quality while adapting network delivery to actual requirements, resolving the contradiction between operational simplicity and quality consistency.
Data Source
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AI summary
Embodiments of the present invention provide a compact representation of a cumulative bit curve formed from piecewise straight line approximations between upper and lower bounds about an actual cumulative bit curve (CBC). In one embodiment the lower bounds are found by applying a constraint such that if a delivery rate was to be calculated using the count at the lower bound it would result in a delivery rate which was greater than the delivery rate that would be calculated using the actual CBC data by a particular amount, for example 10 %. The actual CBC data is then used as an upper bound. As a result, the approximated CBC will lie for each GoP between the actual CBC value and the lower bound, with the result that one can be certain that any data rate that is calculated using the approximation will be at least as high as a data rate that is calculated using the actual CBC data. In terms of line- fitting algorithms that are used, several different algorithms may be used of differing complexities, with the intention of trying to minimise the number of end-points required of within the piecewise approximation.