blob: 1489257600a30463fc2bd9772dae9cf0e562f29b [file] [log] [blame]
/*
* Copyright (c) 2016 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 "modules/congestion_controller/trendline_estimator.h"
#include <algorithm>
#include "api/optional.h"
#include "modules/remote_bitrate_estimator/test/bwe_test_logging.h"
#include "rtc_base/checks.h"
namespace webrtc {
namespace {
rtc::Optional<double> LinearFitSlope(
const std::deque<std::pair<double, double>>& points) {
RTC_DCHECK(points.size() >= 2);
// Compute the "center of mass".
double sum_x = 0;
double sum_y = 0;
for (const auto& point : points) {
sum_x += point.first;
sum_y += point.second;
}
double x_avg = sum_x / points.size();
double y_avg = sum_y / points.size();
// Compute the slope k = \sum (x_i-x_avg)(y_i-y_avg) / \sum (x_i-x_avg)^2
double numerator = 0;
double denominator = 0;
for (const auto& point : points) {
numerator += (point.first - x_avg) * (point.second - y_avg);
denominator += (point.first - x_avg) * (point.first - x_avg);
}
if (denominator == 0)
return rtc::Optional<double>();
return rtc::Optional<double>(numerator / denominator);
}
} // namespace
enum { kDeltaCounterMax = 1000 };
TrendlineEstimator::TrendlineEstimator(size_t window_size,
double smoothing_coef,
double threshold_gain)
: window_size_(window_size),
smoothing_coef_(smoothing_coef),
threshold_gain_(threshold_gain),
num_of_deltas_(0),
first_arrival_time_ms(-1),
accumulated_delay_(0),
smoothed_delay_(0),
delay_hist_(),
trendline_(0) {}
TrendlineEstimator::~TrendlineEstimator() {}
void TrendlineEstimator::Update(double recv_delta_ms,
double send_delta_ms,
int64_t arrival_time_ms) {
const double delta_ms = recv_delta_ms - send_delta_ms;
++num_of_deltas_;
if (num_of_deltas_ > kDeltaCounterMax)
num_of_deltas_ = kDeltaCounterMax;
if (first_arrival_time_ms == -1)
first_arrival_time_ms = arrival_time_ms;
// Exponential backoff filter.
accumulated_delay_ += delta_ms;
BWE_TEST_LOGGING_PLOT(1, "accumulated_delay_ms", arrival_time_ms,
accumulated_delay_);
smoothed_delay_ = smoothing_coef_ * smoothed_delay_ +
(1 - smoothing_coef_) * accumulated_delay_;
BWE_TEST_LOGGING_PLOT(1, "smoothed_delay_ms", arrival_time_ms,
smoothed_delay_);
// Simple linear regression.
delay_hist_.push_back(std::make_pair(
static_cast<double>(arrival_time_ms - first_arrival_time_ms),
smoothed_delay_));
if (delay_hist_.size() > window_size_)
delay_hist_.pop_front();
if (delay_hist_.size() == window_size_) {
// Only update trendline_ if it is possible to fit a line to the data.
trendline_ = LinearFitSlope(delay_hist_).value_or(trendline_);
}
BWE_TEST_LOGGING_PLOT(1, "trendline_slope", arrival_time_ms, trendline_);
}
} // namespace webrtc