afaef8bbeb
Parse the estimation parameters from the field trial string. BUG=webrtc:6690 Review-Url: https://codereview.webrtc.org/2489323002 Cr-Commit-Position: refs/heads/master@{#15126}
88 lines
2.8 KiB
C++
88 lines
2.8 KiB
C++
/*
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* Copyright (c) 2016 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "webrtc/modules/congestion_controller/trendline_estimator.h"
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#include <algorithm>
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#include "webrtc/base/checks.h"
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#include "webrtc/modules/remote_bitrate_estimator/test/bwe_test_logging.h"
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namespace webrtc {
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namespace {
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double LinearFitSlope(const std::list<std::pair<double, double>> points) {
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RTC_DCHECK(points.size() >= 2);
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// Compute the "center of mass".
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double sum_x = 0;
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double sum_y = 0;
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for (const auto& point : points) {
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sum_x += point.first;
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sum_y += point.second;
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}
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double x_avg = sum_x / points.size();
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double y_avg = sum_y / points.size();
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// Compute the slope k = \sum (x_i-x_avg)(y_i-y_avg) / \sum (x_i-x_avg)^2
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double numerator = 0;
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double denominator = 0;
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for (const auto& point : points) {
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numerator += (point.first - x_avg) * (point.second - y_avg);
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denominator += (point.first - x_avg) * (point.first - x_avg);
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}
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return numerator / denominator;
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}
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} // namespace
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enum { kDeltaCounterMax = 1000 };
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TrendlineEstimator::TrendlineEstimator(size_t window_size,
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double smoothing_coef,
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double threshold_gain)
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: window_size_(window_size),
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smoothing_coef_(smoothing_coef),
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threshold_gain_(threshold_gain),
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num_of_deltas_(0),
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accumulated_delay_(0),
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smoothed_delay_(0),
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delay_hist_(),
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trendline_(0) {}
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TrendlineEstimator::~TrendlineEstimator() {}
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void TrendlineEstimator::Update(double recv_delta_ms,
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double send_delta_ms,
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double now_ms) {
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const double delta_ms = recv_delta_ms - send_delta_ms;
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++num_of_deltas_;
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if (num_of_deltas_ > kDeltaCounterMax) {
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num_of_deltas_ = kDeltaCounterMax;
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}
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// Exponential backoff filter.
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accumulated_delay_ += delta_ms;
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BWE_TEST_LOGGING_PLOT(1, "accumulated_delay_ms", now_ms, accumulated_delay_);
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smoothed_delay_ = smoothing_coef_ * smoothed_delay_ +
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(1 - smoothing_coef_) * accumulated_delay_;
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BWE_TEST_LOGGING_PLOT(1, "smoothed_delay_ms", now_ms, smoothed_delay_);
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// Simple linear regression.
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delay_hist_.push_back(std::make_pair(now_ms, smoothed_delay_));
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if (delay_hist_.size() > window_size_) {
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delay_hist_.pop_front();
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}
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if (delay_hist_.size() == window_size_) {
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trendline_ = LinearFitSlope(delay_hist_);
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}
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BWE_TEST_LOGGING_PLOT(1, "trendline_slope", now_ms, trendline_);
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}
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} // namespace webrtc
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