| Index: webrtc/modules/congestion_controller/delay_based_bwe.cc
|
| diff --git a/webrtc/modules/congestion_controller/delay_based_bwe.cc b/webrtc/modules/congestion_controller/delay_based_bwe.cc
|
| index e8d9d21050f21d1b931999b758979b95ec953bcb..042d7fa74aaf99cc20bdebdcaccf0537f9561b75 100644
|
| --- a/webrtc/modules/congestion_controller/delay_based_bwe.cc
|
| +++ b/webrtc/modules/congestion_controller/delay_based_bwe.cc
|
| @@ -42,86 +42,12 @@ constexpr int kInitialRateWindowMs = 500;
|
| constexpr int kRateWindowMs = 150;
|
|
|
| // Parameters for linear least squares fit of regression line to noisy data.
|
| -constexpr size_t kDefaultTrendlineWindowSize = 15;
|
| +constexpr size_t kDefaultTrendlineWindowSize = 20;
|
| constexpr double kDefaultTrendlineSmoothingCoeff = 0.9;
|
| constexpr double kDefaultTrendlineThresholdGain = 4.0;
|
|
|
| -// Parameters for Theil-Sen robust fitting of line to noisy data.
|
| -constexpr size_t kDefaultMedianSlopeWindowSize = 20;
|
| -constexpr double kDefaultMedianSlopeThresholdGain = 4.0;
|
| -
|
| constexpr int kMaxConsecutiveFailedLookups = 5;
|
|
|
| -const char kBitrateEstimateExperiment[] = "WebRTC-ImprovedBitrateEstimate";
|
| -const char kBweTrendlineFilterExperiment[] = "WebRTC-BweTrendlineFilter";
|
| -const char kBweMedianSlopeFilterExperiment[] = "WebRTC-BweMedianSlopeFilter";
|
| -
|
| -bool BitrateEstimateExperimentIsEnabled() {
|
| - return webrtc::field_trial::IsEnabled(kBitrateEstimateExperiment);
|
| -}
|
| -
|
| -bool TrendlineFilterExperimentIsEnabled() {
|
| - std::string experiment_string =
|
| - webrtc::field_trial::FindFullName(kBweTrendlineFilterExperiment);
|
| - // The experiment is enabled iff the field trial string begins with "Enabled".
|
| - return experiment_string.find("Enabled") == 0;
|
| -}
|
| -
|
| -bool MedianSlopeFilterExperimentIsEnabled() {
|
| - std::string experiment_string =
|
| - webrtc::field_trial::FindFullName(kBweMedianSlopeFilterExperiment);
|
| - // The experiment is enabled iff the field trial string begins with "Enabled".
|
| - return experiment_string.find("Enabled") == 0;
|
| -}
|
| -
|
| -bool ReadTrendlineFilterExperimentParameters(size_t* window_size,
|
| - double* smoothing_coef,
|
| - double* threshold_gain) {
|
| - RTC_DCHECK(TrendlineFilterExperimentIsEnabled());
|
| - RTC_DCHECK(!MedianSlopeFilterExperimentIsEnabled());
|
| - RTC_DCHECK(window_size != nullptr);
|
| - RTC_DCHECK(smoothing_coef != nullptr);
|
| - RTC_DCHECK(threshold_gain != nullptr);
|
| - std::string experiment_string =
|
| - webrtc::field_trial::FindFullName(kBweTrendlineFilterExperiment);
|
| - int parsed_values = sscanf(experiment_string.c_str(), "Enabled-%zu,%lf,%lf",
|
| - window_size, smoothing_coef, threshold_gain);
|
| - if (parsed_values == 3) {
|
| - RTC_CHECK_GT(*window_size, 1) << "Need at least 2 points to fit a line.";
|
| - RTC_CHECK(0 <= *smoothing_coef && *smoothing_coef <= 1)
|
| - << "Coefficient needs to be between 0 and 1 for weighted average.";
|
| - RTC_CHECK_GT(*threshold_gain, 0) << "Threshold gain needs to be positive.";
|
| - return true;
|
| - }
|
| - LOG(LS_WARNING) << "Failed to parse parameters for BweTrendlineFilter "
|
| - "experiment from field trial string. Using default.";
|
| - *window_size = kDefaultTrendlineWindowSize;
|
| - *smoothing_coef = kDefaultTrendlineSmoothingCoeff;
|
| - *threshold_gain = kDefaultTrendlineThresholdGain;
|
| - return false;
|
| -}
|
| -
|
| -bool ReadMedianSlopeFilterExperimentParameters(size_t* window_size,
|
| - double* threshold_gain) {
|
| - RTC_DCHECK(!TrendlineFilterExperimentIsEnabled());
|
| - RTC_DCHECK(MedianSlopeFilterExperimentIsEnabled());
|
| - RTC_DCHECK(window_size != nullptr);
|
| - RTC_DCHECK(threshold_gain != nullptr);
|
| - std::string experiment_string =
|
| - webrtc::field_trial::FindFullName(kBweMedianSlopeFilterExperiment);
|
| - int parsed_values = sscanf(experiment_string.c_str(), "Enabled-%zu,%lf",
|
| - window_size, threshold_gain);
|
| - if (parsed_values == 2) {
|
| - RTC_CHECK_GT(*window_size, 1) << "Need at least 2 points to fit a line.";
|
| - RTC_CHECK_GT(*threshold_gain, 0) << "Threshold gain needs to be positive.";
|
| - return true;
|
| - }
|
| - LOG(LS_WARNING) << "Failed to parse parameters for BweMedianSlopeFilter "
|
| - "experiment from field trial string. Using default.";
|
| - *window_size = kDefaultMedianSlopeWindowSize;
|
| - *threshold_gain = kDefaultMedianSlopeThresholdGain;
|
| - return false;
|
| -}
|
|
|
| class PacketFeedbackComparator {
|
| public:
|
| @@ -153,19 +79,9 @@ DelayBasedBwe::BitrateEstimator::BitrateEstimator()
|
| current_win_ms_(0),
|
| prev_time_ms_(-1),
|
| bitrate_estimate_(-1.0f),
|
| - bitrate_estimate_var_(50.0f),
|
| - old_estimator_(kBitrateWindowMs, 8000),
|
| - in_experiment_(BitrateEstimateExperimentIsEnabled()) {}
|
| + bitrate_estimate_var_(50.0f) {}
|
|
|
| void DelayBasedBwe::BitrateEstimator::Update(int64_t now_ms, int bytes) {
|
| - if (!in_experiment_) {
|
| - old_estimator_.Update(bytes, now_ms);
|
| - rtc::Optional<uint32_t> rate = old_estimator_.Rate(now_ms);
|
| - bitrate_estimate_ = -1.0f;
|
| - if (rate)
|
| - bitrate_estimate_ = *rate / 1000.0f;
|
| - return;
|
| - }
|
| int rate_window_ms = kRateWindowMs;
|
| // We use a larger window at the beginning to get a more stable sample that
|
| // we can use to initialize the estimate.
|
| @@ -231,12 +147,9 @@ rtc::Optional<uint32_t> DelayBasedBwe::BitrateEstimator::bitrate_bps() const {
|
| }
|
|
|
| DelayBasedBwe::DelayBasedBwe(RtcEventLog* event_log, const Clock* clock)
|
| - : in_trendline_experiment_(TrendlineFilterExperimentIsEnabled()),
|
| - in_median_slope_experiment_(MedianSlopeFilterExperimentIsEnabled()),
|
| - event_log_(event_log),
|
| + : event_log_(event_log),
|
| clock_(clock),
|
| inter_arrival_(),
|
| - kalman_estimator_(),
|
| trendline_estimator_(),
|
| detector_(),
|
| receiver_incoming_bitrate_(),
|
| @@ -248,29 +161,10 @@ DelayBasedBwe::DelayBasedBwe(RtcEventLog* event_log, const Clock* clock)
|
| trendline_smoothing_coeff_(kDefaultTrendlineSmoothingCoeff),
|
| trendline_threshold_gain_(kDefaultTrendlineThresholdGain),
|
| probing_interval_estimator_(&rate_control_),
|
| - median_slope_window_size_(kDefaultMedianSlopeWindowSize),
|
| - median_slope_threshold_gain_(kDefaultMedianSlopeThresholdGain),
|
| consecutive_delayed_feedbacks_(0),
|
| last_logged_bitrate_(0),
|
| last_logged_state_(kBwNormal) {
|
| - if (in_trendline_experiment_) {
|
| - ReadTrendlineFilterExperimentParameters(&trendline_window_size_,
|
| - &trendline_smoothing_coeff_,
|
| - &trendline_threshold_gain_);
|
| - LOG(LS_INFO) << "Trendline filter experiment enabled with parameters "
|
| - << trendline_window_size_ << ',' << trendline_smoothing_coeff_
|
| - << ',' << trendline_threshold_gain_;
|
| - }
|
| - if (in_median_slope_experiment_) {
|
| - ReadMedianSlopeFilterExperimentParameters(&median_slope_window_size_,
|
| - &median_slope_threshold_gain_);
|
| - LOG(LS_INFO) << "Median-slope filter experiment enabled with parameters "
|
| - << median_slope_window_size_ << ','
|
| - << median_slope_threshold_gain_;
|
| - }
|
| - if (!in_trendline_experiment_ && !in_median_slope_experiment_) {
|
| - LOG(LS_INFO) << "No overuse experiment enabled. Using Kalman filter.";
|
| - }
|
| + LOG(LS_INFO) << "Using Trendline filter for delay change estimation.";
|
|
|
| network_thread_.DetachFromThread();
|
| }
|
| @@ -343,12 +237,9 @@ DelayBasedBwe::Result DelayBasedBwe::IncomingPacketFeedback(
|
| inter_arrival_.reset(
|
| new InterArrival((kTimestampGroupLengthMs << kInterArrivalShift) / 1000,
|
| kTimestampToMs, true));
|
| - kalman_estimator_.reset(new OveruseEstimator(OverUseDetectorOptions()));
|
| trendline_estimator_.reset(new TrendlineEstimator(
|
| trendline_window_size_, trendline_smoothing_coeff_,
|
| trendline_threshold_gain_));
|
| - median_slope_estimator_.reset(new MedianSlopeEstimator(
|
| - median_slope_window_size_, median_slope_threshold_gain_));
|
| }
|
| last_seen_packet_ms_ = now_ms;
|
|
|
| @@ -370,26 +261,11 @@ DelayBasedBwe::Result DelayBasedBwe::IncomingPacketFeedback(
|
| now_ms, packet_feedback.payload_size,
|
| &ts_delta, &t_delta, &size_delta)) {
|
| double ts_delta_ms = (1000.0 * ts_delta) / (1 << kInterArrivalShift);
|
| - if (in_trendline_experiment_) {
|
| - trendline_estimator_->Update(t_delta, ts_delta_ms,
|
| - packet_feedback.arrival_time_ms);
|
| - detector_.Detect(trendline_estimator_->trendline_slope(), ts_delta_ms,
|
| - trendline_estimator_->num_of_deltas(),
|
| - packet_feedback.arrival_time_ms);
|
| - } else if (in_median_slope_experiment_) {
|
| - median_slope_estimator_->Update(t_delta, ts_delta_ms,
|
| - packet_feedback.arrival_time_ms);
|
| - detector_.Detect(median_slope_estimator_->trendline_slope(), ts_delta_ms,
|
| - median_slope_estimator_->num_of_deltas(),
|
| - packet_feedback.arrival_time_ms);
|
| - } else {
|
| - kalman_estimator_->Update(t_delta, ts_delta_ms, size_delta,
|
| - detector_.State(),
|
| - packet_feedback.arrival_time_ms);
|
| - detector_.Detect(kalman_estimator_->offset(), ts_delta_ms,
|
| - kalman_estimator_->num_of_deltas(),
|
| - packet_feedback.arrival_time_ms);
|
| - }
|
| + trendline_estimator_->Update(t_delta, ts_delta_ms,
|
| + packet_feedback.arrival_time_ms);
|
| + detector_.Detect(trendline_estimator_->trendline_slope(), ts_delta_ms,
|
| + trendline_estimator_->num_of_deltas(),
|
| + packet_feedback.arrival_time_ms);
|
| }
|
|
|
| int probing_bps = 0;
|
|
|