webrtc/modules/remote_bitrate_estimator/overuse_detector.cc - src - Git at Google

 /*
  *  Copyright (c) 2012 The WebRTC project authors. All Rights Reserved.
  *
  *  Use of this source code is governed by a BSD-style license
  *  that can be found in the LICENSE file in the root of the source
  *  tree. An additional intellectual property rights grant can be found
  *  in the file PATENTS.  All contributing project authors may
  *  be found in the AUTHORS file in the root of the source tree.
  */

 #include <math.h>
 #include <stdlib.h>  // fabsf

 #include "webrtc/modules/remote_bitrate_estimator/overuse_detector.h"
 #include "webrtc/modules/remote_bitrate_estimator/remote_rate_control.h"
 #include "webrtc/modules/rtp_rtcp/source/rtp_utility.h"
 #include "webrtc/system_wrappers/interface/trace.h"

 enum { kOverUsingTimeThreshold = 100 };
 enum { kMinFramePeriodHistoryLength = 60 };

 namespace webrtc {
 OveruseDetector::OveruseDetector(const OverUseDetectorOptions& options)
     : options_(options),
       current_frame_(),
       prev_frame_(),
       num_of_deltas_(0),
       slope_(options_.initial_slope),
       offset_(options_.initial_offset),
       E_(),
       process_noise_(),
       avg_noise_(options_.initial_avg_noise),
       var_noise_(options_.initial_var_noise),
       threshold_(options_.initial_threshold),
       ts_delta_hist_(),
       prev_offset_(0.0),
       time_over_using_(-1),
       over_use_counter_(0),
       hypothesis_(kBwNormal) {
   memcpy(E_, options_.initial_e, sizeof(E_));
   memcpy(process_noise_, options_.initial_process_noise,
          sizeof(process_noise_));
 }

 OveruseDetector::~OveruseDetector() {
   ts_delta_hist_.clear();
 }

 void OveruseDetector::Update(uint16_t packet_size,
                              int64_t timestamp_ms,
                              uint32_t timestamp,
                              const int64_t arrival_time_ms) {
   bool new_timestamp = (timestamp != current_frame_.timestamp);
   if (timestamp_ms >= 0) {
     if (prev_frame_.timestamp_ms == -1 && current_frame_.timestamp_ms == -1) {
       SwitchTimeBase();
     }
     new_timestamp = (timestamp_ms != current_frame_.timestamp_ms);
   }
   if (current_frame_.timestamp == -1) {
     // This is the first incoming packet. We don't have enough data to update
     // the filter, so we store it until we have two frames of data to process.
     current_frame_.timestamp = timestamp;
     current_frame_.timestamp_ms = timestamp_ms;
   } else if (!PacketInOrder(timestamp, timestamp_ms)) {
     return;
   } else if (new_timestamp) {
     // First packet of a later frame, the previous frame sample is ready.
     if (prev_frame_.complete_time_ms >= 0) {  // This is our second frame.
       int64_t t_delta = 0;
       double ts_delta = 0;
       TimeDeltas(current_frame_, prev_frame_, &t_delta, &ts_delta);
       UpdateKalman(t_delta, ts_delta, current_frame_.size, prev_frame_.size);
     }
     prev_frame_ = current_frame_;
     // The new timestamp is now the current frame.
     current_frame_.timestamp = timestamp;
     current_frame_.timestamp_ms = timestamp_ms;
     current_frame_.size = 0;
   }
   // Accumulate the frame size
   current_frame_.size += packet_size;
   current_frame_.complete_time_ms = arrival_time_ms;
 }

 BandwidthUsage OveruseDetector::State() const {
   return hypothesis_;
 }

 double OveruseDetector::NoiseVar() const {
   return var_noise_;
 }

 void OveruseDetector::SetRateControlRegion(RateControlRegion region) {
   switch (region) {
     case kRcMaxUnknown: {
       threshold_ = options_.initial_threshold;
       break;
     }
     case kRcAboveMax:
     case kRcNearMax: {
       threshold_ = options_.initial_threshold / 2;
       break;
     }
   }
 }

 void OveruseDetector::SwitchTimeBase() {
   current_frame_.size = 0;
   current_frame_.complete_time_ms = -1;
   current_frame_.timestamp = -1;
   prev_frame_ = current_frame_;
 }

 void OveruseDetector::TimeDeltas(const FrameSample& current_frame,
                                  const FrameSample& prev_frame,
                                  int64_t* t_delta,
                                  double* ts_delta) {
   assert(t_delta);
   assert(ts_delta);
   num_of_deltas_++;
   if (num_of_deltas_ > 1000) {
     num_of_deltas_ = 1000;
   }
   if (current_frame.timestamp_ms == -1) {
     uint32_t timestamp_diff = current_frame.timestamp - prev_frame.timestamp;
     *ts_delta = timestamp_diff / 90.0;
   } else {
     *ts_delta = current_frame.timestamp_ms - prev_frame.timestamp_ms;
   }
   *t_delta = current_frame.complete_time_ms - prev_frame.complete_time_ms;
   assert(*ts_delta > 0);
 }

 bool OveruseDetector::PacketInOrder(uint32_t timestamp, int64_t timestamp_ms) {
   if (current_frame_.timestamp_ms == -1 && current_frame_.timestamp > -1) {
     return InOrderTimestamp(timestamp, current_frame_.timestamp);
   } else if (current_frame_.timestamp_ms > 0) {
     // Using timestamps converted to NTP time.
     return timestamp_ms > current_frame_.timestamp_ms;
   }
   // This is the first packet.
   return true;
 }

 bool OveruseDetector::InOrderTimestamp(uint32_t timestamp,
                                        uint32_t prev_timestamp) {
   uint32_t timestamp_diff = timestamp - prev_timestamp;
   // Assume that a diff this big must be due to reordering. Don't update
   // with reordered samples.
   return (timestamp_diff < 0x80000000);
 }

 double OveruseDetector::CurrentDrift() {
   return 1.0;
 }

 void OveruseDetector::UpdateKalman(int64_t t_delta,
                                    double ts_delta,
                                    uint32_t frame_size,
                                    uint32_t prev_frame_size) {
   const double min_frame_period = UpdateMinFramePeriod(ts_delta);
   const double drift = CurrentDrift();
   // Compensate for drift
   const double t_ts_delta = t_delta - ts_delta / drift;
   double fs_delta = static_cast<double>(frame_size) - prev_frame_size;

   // Update the Kalman filter
   const double scale_factor =  min_frame_period / (1000.0 / 30.0);
   E_[0][0] += process_noise_[0] * scale_factor;
   E_[1][1] += process_noise_[1] * scale_factor;

   if ((hypothesis_ == kBwOverusing && offset_ < prev_offset_) ||
       (hypothesis_ == kBwUnderusing && offset_ > prev_offset_)) {
     E_[1][1] += 10 * process_noise_[1] * scale_factor;
   }

   const double h[2] = {fs_delta, 1.0};
   const double Eh[2] = {E_[0][0]*h[0] + E_[0][1]*h[1],
                         E_[1][0]*h[0] + E_[1][1]*h[1]};

   const double residual = t_ts_delta - slope_*h[0] - offset_;

   const bool stable_state =
       (BWE_MIN(num_of_deltas_, 60) * fabs(offset_) < threshold_);
   // We try to filter out very late frames. For instance periodic key
   // frames doesn't fit the Gaussian model well.
   if (fabs(residual) < 3 * sqrt(var_noise_)) {
     UpdateNoiseEstimate(residual, min_frame_period, stable_state);
   } else {
     UpdateNoiseEstimate(3 * sqrt(var_noise_), min_frame_period, stable_state);
   }

   const double denom = var_noise_ + h[0]*Eh[0] + h[1]*Eh[1];

   const double K[2] = {Eh[0] / denom,
                        Eh[1] / denom};

   const double IKh[2][2] = {{1.0 - K[0]*h[0], -K[0]*h[1]},
                             {-K[1]*h[0], 1.0 - K[1]*h[1]}};
   const double e00 = E_[0][0];
   const double e01 = E_[0][1];

   // Update state
   E_[0][0] = e00 * IKh[0][0] + E_[1][0] * IKh[0][1];
   E_[0][1] = e01 * IKh[0][0] + E_[1][1] * IKh[0][1];
   E_[1][0] = e00 * IKh[1][0] + E_[1][0] * IKh[1][1];
   E_[1][1] = e01 * IKh[1][0] + E_[1][1] * IKh[1][1];

   // Covariance matrix, must be positive semi-definite
   assert(E_[0][0] + E_[1][1] >= 0 &&
          E_[0][0] * E_[1][1] - E_[0][1] * E_[1][0] >= 0 &&
          E_[0][0] >= 0);

   slope_ = slope_ + K[0] * residual;
   prev_offset_ = offset_;
   offset_ = offset_ + K[1] * residual;

   Detect(ts_delta);
 }

 double OveruseDetector::UpdateMinFramePeriod(double ts_delta) {
   double min_frame_period = ts_delta;
   if (ts_delta_hist_.size() >= kMinFramePeriodHistoryLength) {
     std::list<double>::iterator first_item = ts_delta_hist_.begin();
     ts_delta_hist_.erase(first_item);
   }
   std::list<double>::iterator it = ts_delta_hist_.begin();
   for (; it != ts_delta_hist_.end(); it++) {
     min_frame_period = BWE_MIN(*it, min_frame_period);
   }
   ts_delta_hist_.push_back(ts_delta);
   return min_frame_period;
 }

 void OveruseDetector::UpdateNoiseEstimate(double residual,
                                           double ts_delta,
                                           bool stable_state) {
   if (!stable_state) {
     return;
   }
   // Faster filter during startup to faster adapt to the jitter level
   // of the network alpha is tuned for 30 frames per second, but
   double alpha = 0.01;
   if (num_of_deltas_ > 10*30) {
     alpha = 0.002;
   }
   // Only update the noise estimate if we're not over-using
   // beta is a function of alpha and the time delta since
   // the previous update.
   const double beta = pow(1 - alpha, ts_delta * 30.0 / 1000.0);
   avg_noise_ = beta * avg_noise_
               + (1 - beta) * residual;
   var_noise_ = beta * var_noise_
               + (1 - beta) * (avg_noise_ - residual) * (avg_noise_ - residual);
   if (var_noise_ < 1e-7) {
     var_noise_ = 1e-7;
   }
 }

 BandwidthUsage OveruseDetector::Detect(double ts_delta) {
   if (num_of_deltas_ < 2) {
     return kBwNormal;
   }
   const double T = BWE_MIN(num_of_deltas_, 60) * offset_;
   if (fabs(T) > threshold_) {
     if (offset_ > 0) {
       if (time_over_using_ == -1) {
         // Initialize the timer. Assume that we've been
         // over-using half of the time since the previous
         // sample.
         time_over_using_ = ts_delta / 2;
       } else {
         // Increment timer
         time_over_using_ += ts_delta;
       }
       over_use_counter_++;
       if (time_over_using_ > kOverUsingTimeThreshold
           && over_use_counter_ > 1) {
         if (offset_ >= prev_offset_) {
           time_over_using_ = 0;
           over_use_counter_ = 0;
           hypothesis_ = kBwOverusing;
         }
       }
     } else {
       time_over_using_ = -1;
       over_use_counter_ = 0;
       hypothesis_ = kBwUnderusing;
     }
   } else {
     time_over_using_ = -1;
     over_use_counter_ = 0;
     hypothesis_ = kBwNormal;
   }
   return hypothesis_;
 }
 }  // namespace webrtc
	/*
	* Copyright (c) 2012 The WebRTC project authors. All Rights Reserved.
	*
	* Use of this source code is governed by a BSD-style license
	* that can be found in the LICENSE file in the root of the source
	* tree. An additional intellectual property rights grant can be found
	* in the file PATENTS. All contributing project authors may
	* be found in the AUTHORS file in the root of the source tree.
	*/

	#include <math.h>
	#include <stdlib.h> // fabsf

	#include "webrtc/modules/remote_bitrate_estimator/overuse_detector.h"
	#include "webrtc/modules/remote_bitrate_estimator/remote_rate_control.h"
	#include "webrtc/modules/rtp_rtcp/source/rtp_utility.h"
	#include "webrtc/system_wrappers/interface/trace.h"

	enum { kOverUsingTimeThreshold = 100 };
	enum { kMinFramePeriodHistoryLength = 60 };

	namespace webrtc {
	OveruseDetector::OveruseDetector(const OverUseDetectorOptions& options)
	: options_(options),
	current_frame_(),
	prev_frame_(),
	num_of_deltas_(0),
	slope_(options_.initial_slope),
	offset_(options_.initial_offset),
	E_(),
	process_noise_(),
	avg_noise_(options_.initial_avg_noise),
	var_noise_(options_.initial_var_noise),
	threshold_(options_.initial_threshold),
	ts_delta_hist_(),
	prev_offset_(0.0),
	time_over_using_(-1),
	over_use_counter_(0),
	hypothesis_(kBwNormal) {
	memcpy(E_, options_.initial_e, sizeof(E_));
	memcpy(process_noise_, options_.initial_process_noise,
	sizeof(process_noise_));
	}

	OveruseDetector::~OveruseDetector() {
	ts_delta_hist_.clear();
	}

	void OveruseDetector::Update(uint16_t packet_size,
	int64_t timestamp_ms,
	uint32_t timestamp,
	const int64_t arrival_time_ms) {
	bool new_timestamp = (timestamp != current_frame_.timestamp);
	if (timestamp_ms >= 0) {
	if (prev_frame_.timestamp_ms == -1 && current_frame_.timestamp_ms == -1) {
	SwitchTimeBase();
	}
	new_timestamp = (timestamp_ms != current_frame_.timestamp_ms);
	}
	if (current_frame_.timestamp == -1) {
	// This is the first incoming packet. We don't have enough data to update
	// the filter, so we store it until we have two frames of data to process.
	current_frame_.timestamp = timestamp;
	current_frame_.timestamp_ms = timestamp_ms;
	} else if (!PacketInOrder(timestamp, timestamp_ms)) {
	return;
	} else if (new_timestamp) {
	// First packet of a later frame, the previous frame sample is ready.
	if (prev_frame_.complete_time_ms >= 0) { // This is our second frame.
	int64_t t_delta = 0;
	double ts_delta = 0;
	TimeDeltas(current_frame_, prev_frame_, &t_delta, &ts_delta);
	UpdateKalman(t_delta, ts_delta, current_frame_.size, prev_frame_.size);
	}
	prev_frame_ = current_frame_;
	// The new timestamp is now the current frame.
	current_frame_.timestamp = timestamp;
	current_frame_.timestamp_ms = timestamp_ms;
	current_frame_.size = 0;
	}
	// Accumulate the frame size
	current_frame_.size += packet_size;
	current_frame_.complete_time_ms = arrival_time_ms;
	}

	BandwidthUsage OveruseDetector::State() const {
	return hypothesis_;
	}

	double OveruseDetector::NoiseVar() const {
	return var_noise_;
	}

	void OveruseDetector::SetRateControlRegion(RateControlRegion region) {
	switch (region) {
	case kRcMaxUnknown: {
	threshold_ = options_.initial_threshold;
	break;
	}
	case kRcAboveMax:
	case kRcNearMax: {
	threshold_ = options_.initial_threshold / 2;
	break;
	}
	}
	}

	void OveruseDetector::SwitchTimeBase() {
	current_frame_.size = 0;
	current_frame_.complete_time_ms = -1;
	current_frame_.timestamp = -1;
	prev_frame_ = current_frame_;
	}

	void OveruseDetector::TimeDeltas(const FrameSample& current_frame,
	const FrameSample& prev_frame,
	int64_t* t_delta,
	double* ts_delta) {
	assert(t_delta);
	assert(ts_delta);
	num_of_deltas_++;
	if (num_of_deltas_ > 1000) {
	num_of_deltas_ = 1000;
	}
	if (current_frame.timestamp_ms == -1) {
	uint32_t timestamp_diff = current_frame.timestamp - prev_frame.timestamp;
	*ts_delta = timestamp_diff / 90.0;
	} else {
	*ts_delta = current_frame.timestamp_ms - prev_frame.timestamp_ms;
	}
	*t_delta = current_frame.complete_time_ms - prev_frame.complete_time_ms;
	assert(*ts_delta > 0);
	}

	bool OveruseDetector::PacketInOrder(uint32_t timestamp, int64_t timestamp_ms) {
	if (current_frame_.timestamp_ms == -1 && current_frame_.timestamp > -1) {
	return InOrderTimestamp(timestamp, current_frame_.timestamp);
	} else if (current_frame_.timestamp_ms > 0) {
	// Using timestamps converted to NTP time.
	return timestamp_ms > current_frame_.timestamp_ms;
	}
	// This is the first packet.
	return true;
	}

	bool OveruseDetector::InOrderTimestamp(uint32_t timestamp,
	uint32_t prev_timestamp) {
	uint32_t timestamp_diff = timestamp - prev_timestamp;
	// Assume that a diff this big must be due to reordering. Don't update
	// with reordered samples.
	return (timestamp_diff < 0x80000000);
	}

	double OveruseDetector::CurrentDrift() {
	return 1.0;
	}

	void OveruseDetector::UpdateKalman(int64_t t_delta,
	double ts_delta,
	uint32_t frame_size,
	uint32_t prev_frame_size) {
	const double min_frame_period = UpdateMinFramePeriod(ts_delta);
	const double drift = CurrentDrift();
	// Compensate for drift
	const double t_ts_delta = t_delta - ts_delta / drift;
	double fs_delta = static_cast<double>(frame_size) - prev_frame_size;

	// Update the Kalman filter
	const double scale_factor = min_frame_period / (1000.0 / 30.0);
	E_[0][0] += process_noise_[0] * scale_factor;
	E_[1][1] += process_noise_[1] * scale_factor;

	if ((hypothesis_ == kBwOverusing && offset_ < prev_offset_) \|\|
	(hypothesis_ == kBwUnderusing && offset_ > prev_offset_)) {
	E_[1][1] += 10 * process_noise_[1] * scale_factor;
	}

	const double h[2] = {fs_delta, 1.0};
	const double Eh[2] = {E_[0][0]h[0] + E_[0][1]h[1],
	E_[1][0]h[0] + E_[1][1]h[1]};

	const double residual = t_ts_delta - slope_*h[0] - offset_;

	const bool stable_state =
	(BWE_MIN(num_of_deltas_, 60) * fabs(offset_) < threshold_);
	// We try to filter out very late frames. For instance periodic key
	// frames doesn't fit the Gaussian model well.
	if (fabs(residual) < 3 * sqrt(var_noise_)) {
	UpdateNoiseEstimate(residual, min_frame_period, stable_state);
	} else {
	UpdateNoiseEstimate(3 * sqrt(var_noise_), min_frame_period, stable_state);
	}

	const double denom = var_noise_ + h[0]Eh[0] + h[1]Eh[1];

	const double K[2] = {Eh[0] / denom,
	Eh[1] / denom};

	const double IKh[2][2] = {{1.0 - K[0]h[0], -K[0]h[1]},
	{-K[1]h[0], 1.0 - K[1]h[1]}};
	const double e00 = E_[0][0];
	const double e01 = E_[0][1];

	// Update state
	E_[0][0] = e00 * IKh[0][0] + E_[1][0] * IKh[0][1];
	E_[0][1] = e01 * IKh[0][0] + E_[1][1] * IKh[0][1];
	E_[1][0] = e00 * IKh[1][0] + E_[1][0] * IKh[1][1];
	E_[1][1] = e01 * IKh[1][0] + E_[1][1] * IKh[1][1];

	// Covariance matrix, must be positive semi-definite
	assert(E_[0][0] + E_[1][1] >= 0 &&
	E_[0][0] * E_[1][1] - E_[0][1] * E_[1][0] >= 0 &&
	E_[0][0] >= 0);

	slope_ = slope_ + K[0] * residual;
	prev_offset_ = offset_;
	offset_ = offset_ + K[1] * residual;

	Detect(ts_delta);
	}

	double OveruseDetector::UpdateMinFramePeriod(double ts_delta) {
	double min_frame_period = ts_delta;
	if (ts_delta_hist_.size() >= kMinFramePeriodHistoryLength) {
	std::list<double>::iterator first_item = ts_delta_hist_.begin();
	ts_delta_hist_.erase(first_item);
	}
	std::list<double>::iterator it = ts_delta_hist_.begin();
	for (; it != ts_delta_hist_.end(); it++) {
	min_frame_period = BWE_MIN(*it, min_frame_period);
	}
	ts_delta_hist_.push_back(ts_delta);
	return min_frame_period;
	}

	void OveruseDetector::UpdateNoiseEstimate(double residual,
	double ts_delta,
	bool stable_state) {
	if (!stable_state) {
	return;
	}
	// Faster filter during startup to faster adapt to the jitter level
	// of the network alpha is tuned for 30 frames per second, but
	double alpha = 0.01;
	if (num_of_deltas_ > 10*30) {
	alpha = 0.002;
	}
	// Only update the noise estimate if we're not over-using
	// beta is a function of alpha and the time delta since
	// the previous update.
	const double beta = pow(1 - alpha, ts_delta * 30.0 / 1000.0);
	avg_noise_ = beta * avg_noise_
	+ (1 - beta) * residual;
	var_noise_ = beta * var_noise_
	+ (1 - beta) * (avg_noise_ - residual) * (avg_noise_ - residual);
	if (var_noise_ < 1e-7) {
	var_noise_ = 1e-7;
	}
	}

	BandwidthUsage OveruseDetector::Detect(double ts_delta) {
	if (num_of_deltas_ < 2) {
	return kBwNormal;
	}
	const double T = BWE_MIN(num_of_deltas_, 60) * offset_;
	if (fabs(T) > threshold_) {
	if (offset_ > 0) {
	if (time_over_using_ == -1) {
	// Initialize the timer. Assume that we've been
	// over-using half of the time since the previous
	// sample.
	time_over_using_ = ts_delta / 2;
	} else {
	// Increment timer
	time_over_using_ += ts_delta;
	}
	over_use_counter_++;
	if (time_over_using_ > kOverUsingTimeThreshold
	&& over_use_counter_ > 1) {
	if (offset_ >= prev_offset_) {
	time_over_using_ = 0;
	over_use_counter_ = 0;
	hypothesis_ = kBwOverusing;
	}
	}
	} else {
	time_over_using_ = -1;
	over_use_counter_ = 0;
	hypothesis_ = kBwUnderusing;
	}
	} else {
	time_over_using_ = -1;
	over_use_counter_ = 0;
	hypothesis_ = kBwNormal;
	}
	return hypothesis_;
	}
	} // namespace webrtc