Adaptive Media Encoder Calibration via Feedback Control
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Solution Overview
Problem
Existing calibration systems for media encoders face challenges in accurately estimating load and adapting to real-time capabilities, particularly in multi-threaded and hardware-based systems, due to unreliable static and dynamic metrics, leading to video quality degradation in real-time video telephony.
Innovation Solution
An adaptive control system with an adaptation controller that uses an accumulation parameter, calculated from the incidence of data buffer accumulation events, to dynamically adjust encode parameters and maintain load within predetermined bounds, decoupling the encoder from resource fluctuations and improving calibration accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static metrics (processor frequency, system bus frequency, number of processor cores) are used to estimate encoder capabilities, then the theoretical performance can be measured, but the actual encoder load and performance at a given point in time cannot be accurately determined
Solution Approach 1:
The patent implements feedback mechanisms by monitoring actual encoder performance metrics (frame processing times, queue depths, drop frame rates) and using this information to dynamically adjust encode parameters. This closes the loop between theoretical capabilities and actual performance, allowing the system to adapt to real-time encoder load conditions.
Solution Approach 2:
The encoder system performs self-calibration by automatically measuring its own performance characteristics through built-in monitoring of frame processing times, queue depths, and drop frame rates. This eliminates the need for external testing equipment and allows the system to self-determine its actual capabilities under current operating conditions.
2Reliability
If dynamic metrics (process execution times, processor idle time, current processor frequency) are used to estimate encoder load, then actual performance measurements can be obtained, but multi-threading, virtualization, and processor core switching make these measurements unreliable
Solution Approach 1:
The patent introduces intermediary metrics that indirectly measure encoder load through observable effects such as input queue depth, output queue depth, and frame processing timestamps. These intermediary measurements serve as proxies for direct load measurement, avoiding the complexities of multi-threaded and virtualized processor environments.
Solution Approach 2:
The system replaces direct mechanical measurements of processor load (CPU utilization, thread execution time) with software-based observations of encoder behavior (frame processing times, queue depths, drop frame rates). This substitution allows accurate load estimation without being affected by processor architecture complexities.
3Productivity
If hardware encoders are used, then encoding performance can be maintained independently of CPU load, but conventional static and dynamic metrics become wholly inaccurate for estimating encoder load
Solution Approach 1:
For hardware encoders, the patent uses intermediary measurements such as input queue depth, output queue depth, and frame processing timestamps as proxies for encoder load. These metrics provide indirect but accurate information about hardware encoder utilization without requiring direct access to internal hardware state.
Solution Approach 2:
The system implements feedback loops that monitor the relationship between input frame rates, output frame rates, and queue depths to infer hardware encoder load. This allows the system to adapt encode parameters based on actual hardware encoder performance rather than relying on inaccurate CPU-based metrics.
4Use of energy by stationary object
If voltage and frequency scaling are used to improve energy efficiency, then power consumption is reduced, but static metrics no longer accurately reflect available encoder processing capabilities
Solution Approach 1:
The patent implements dynamic adaptation of encode parameters based on real-time measurements of encoder performance and available resources. The system continuously monitors frame processing times, queue depths, and drop frame rates to determine current encoder capabilities, which may vary due to voltage and frequency scaling, and adjusts encoding parameters accordingly.
Solution Approach 2:
The system changes encode parameters (frame rate, resolution, bitrate) dynamically based on measured encoder performance characteristics. This allows the encoder to operate within its actual capabilities under varying power conditions, maintaining video quality while adapting to energy efficiency constraints.
Data Source
AI summary
A data processing system for calibrating a media codec comprising a sequence of time-stamped frames and comprising: an encoder subsystem configured to perform encoding in accordance with one or more encode parameters; a decoder subsystem; and a calibration system comprising: a data store for storing an encoded media stream; and a calibration monitor configured to, on the media codec entering a calibration mode, cause: the decoder subsystem to decode the encoded media stream so as to generate a decoded media stream; the encoder subsystem to re-encode said decoded media stream; and the re-encoded media stream to pass back into the decoder subsystem; the calibration monitor being configured to, through variation of the encode parameters of the encoder subsystem, identify maximal encode parameters corresponding to the greatest steady-state demand on the media codec that permits decoding of the sequence of time-stamped frames at a rate in accordance with their associated timestamps.