一、背景介绍
一些报文在网络传输中，会存在丢包重传和延时的情况。渲染时需要进行适当缓存，等待丢失被重传的报文或者正在路上传输的报文。
jitter延时计算是确认需要缓存的时间
另外，在检测到帧有重传情况时，也可适当在渲染时间内增加RTT延时时间，等待丢失重传的报文
二、jitter实现原理
JitterDelay由两部分延迟造成：传输大帧引起的延迟和网络噪声引起的延迟。计算公式如下：
其中：
estimate[0]：信道传输速率的倒数
MaxFrameSize：表示自会话开始以来所收到的最大帧size
AvgFrameSize：表示平均帧大小，排除keyframe等超大帧
kNoiseStdDevs： 表示噪声系数2.33
var_noise_ms2_： 表示噪声方差
kNoiseStdDevOffset: 表示噪声扣除常数30
实现函数：
JitterEstimator::CalculateEstimate
1、传输大帧引起的延迟
传输大帧引起的延迟
这个公式的原理是：[milliseconds] = [1 / bytes per millisecond] * [bytes]
实现函数：
double FrameDelayVariationKalmanFilter::GetFrameDelayVariationEstimateSizeBased(double frame_size_variation_bytes) const {// Unit: [1 / bytes per millisecond] * [bytes] = [milliseconds].return estimate_[0] * frame_size_variation_bytes;
}filtered_max_frame_size_bytes
= std::max<double>(kPsi * max_frame_size_bytes_, frame_size.bytes());
constexpr double kPsi = 0.9999;
filtered_avg_frame_size_bytes
是每一帧的加权平均值，但是需要排除key frame这种超大帧
estimate_[0]参数计算：
使用一个简化卡尔曼滤波算法，在处理帧延迟变化(frame_delay_variation_ms)的估计，考虑了帧大小变化(frame_size_variation_bytes)和最大帧大小(max_frame_size_bytes)作为输入参数。
void FrameDelayVariationKalmanFilter::PredictAndUpdate(double frame_delay_variation_ms,double frame_size_variation_bytes,double max_frame_size_bytes,double var_noise) {// Sanity checks.if (max_frame_size_bytes < 1) {return;}if (var_noise <= 0.0) {return;}// This member function follows the data flow in// https://en.wikipedia.org/wiki/Kalman_filter#Details.// 1) Estimate prediction: `x = F*x`.// For this model, there is no need to explicitly predict the estimate, since// the state transition matrix is the identity.// 2) Estimate covariance prediction: `P = F*P*F' + Q`.// Again, since the state transition matrix is the identity, this update// is performed by simply adding the process noise covariance.estimate_cov_[0][0] += process_noise_cov_diag_[0];estimate_cov_[1][1] += process_noise_cov_diag_[1];// 3) Innovation: `y = z - H*x`.// This is the part of the measurement that cannot be explained by the current// estimate.double innovation =frame_delay_variation_ms -GetFrameDelayVariationEstimateTotal(frame_size_variation_bytes);// 4) Innovation variance: `s = H*P*H' + r`.double estim_cov_times_obs[2];estim_cov_times_obs[0] =estimate_cov_[0][0] * frame_size_variation_bytes + estimate_cov_[0][1];estim_cov_times_obs[1] =estimate_cov_[1][0] * frame_size_variation_bytes + estimate_cov_[1][1];double observation_noise_stddev =(300.0 * exp(-fabs(frame_size_variation_bytes) /(1e0 * max_frame_size_bytes)) +1) *sqrt(var_noise);if (observation_noise_stddev < 1.0) {observation_noise_stddev = 1.0;}// TODO(brandtr): Shouldn't we add observation_noise_stddev^2 here? Otherwise,// the dimensional analysis fails.double innovation_var = frame_size_variation_bytes * estim_cov_times_obs[0] +estim_cov_times_obs[1] + observation_noise_stddev;if ((innovation_var < 1e-9 && innovation_var >= 0) ||(innovation_var > -1e-9 && innovation_var <= 0)) {RTC_DCHECK_NOTREACHED();return;}// 5) Optimal Kalman gain: `K = P*H'/s`.// How much to trust the model vs. how much to trust the measurement.double kalman_gain[2];kalman_gain[0] = estim_cov_times_obs[0] / innovation_var;kalman_gain[1] = estim_cov_times_obs[1] / innovation_var;// 6) Estimate update: `x = x + K*y`.// Optimally weight the new information in the innovation and add it to the// old estimate.estimate_[0] += kalman_gain[0] * innovation;estimate_[1] += kalman_gain[1] * innovation;// (This clamping is not part of the linear Kalman filter.)if (estimate_[0] < kMaxBandwidth) {estimate_[0] = kMaxBandwidth;}// 7) Estimate covariance update: `P = (I - K*H)*P`double t00 = estimate_cov_[0][0];double t01 = estimate_cov_[0][1];estimate_cov_[0][0] =(1 - kalman_gain[0] * frame_size_variation_bytes) * t00 -kalman_gain[0] * estimate_cov_[1][0];estimate_cov_[0][1] =(1 - kalman_gain[0] * frame_size_variation_bytes) * t01 -kalman_gain[0] * estimate_cov_[1][1];estimate_cov_[1][0] = estimate_cov_[1][0] * (1 - kalman_gain[1]) -kalman_gain[1] * frame_size_variation_bytes * t00;estimate_cov_[1][1] = estimate_cov_[1][1] * (1 - kalman_gain[1]) -kalman_gain[1] * frame_size_variation_bytes * t01;// Covariance matrix, must be positive semi-definite.RTC_DCHECK(estimate_cov_[0][0] + estimate_cov_[1][1] >= 0 &&estimate_cov_[0][0] * estimate_cov_[1][1] -estimate_cov_[0][1] * estimate_cov_[1][0] >=0 &&estimate_cov_[0][0] >= 0);
}2、网络噪声引起的延迟
网络噪声引起的延迟
constexpr double kNoiseStdDevs = 2.33; //噪声系数
constexpr double kNoiseStdDevOffset = 30.0;//噪声扣除常数
var_noise_ms2_ //噪声方差
实现函数：
噪声方差var_noise_ms2计算
var_noise_ms2 = alpha * var_noise_ms2_ +
(1 - alpha) *(d_dT - avg_noise_ms_) *(d_dT - avg_noise_ms_);
实现函数：JitterEstimator::EstimateRandomJitter
其中：
d_dT = 实际FrameDelay - 评估FrameDelay
在JitterEstimator::UpdateEstimate函数实现
实际FrameDelay = （两帧之间实际接收gap - 两帧之间实际发送gap）
在InterFrameDelayVariationCalculator::Calculate函数实现
absl::optional<TimeDelta> InterFrameDelayVariationCalculator::Calculate(uint32_t rtp_timestamp,Timestamp now) {int64_t rtp_timestamp_unwrapped = unwrapper_.Unwrap(rtp_timestamp);if (!prev_wall_clock_) {prev_wall_clock_ = now;prev_rtp_timestamp_unwrapped_ = rtp_timestamp_unwrapped;// Inter-frame delay variation is undefined for a single frame.// TODO(brandtr): Should this return absl::nullopt instead?return TimeDelta::Zero();}// Account for reordering in jitter variance estimate in the future?// Note that this also captures incomplete frames which are grabbed for// decoding after a later frame has been complete, i.e. real packet losses.uint32_t cropped_prev = static_cast<uint32_t>(prev_rtp_timestamp_unwrapped_);if (rtp_timestamp_unwrapped < prev_rtp_timestamp_unwrapped_ ||!IsNewerTimestamp(rtp_timestamp, cropped_prev)) {return absl::nullopt;}// Compute the compensated timestamp difference.TimeDelta delta_wall = now - *prev_wall_clock_;int64_t d_rtp_ticks = rtp_timestamp_unwrapped - prev_rtp_timestamp_unwrapped_;TimeDelta delta_rtp = d_rtp_ticks / k90kHz;// The inter-frame delay variation is the second order difference between the// RTP and wall clocks of the two frames, or in other words, the first order// difference between `delta_rtp` and `delta_wall`.TimeDelta inter_frame_delay_variation = delta_wall - delta_rtp;prev_wall_clock_ = now;prev_rtp_timestamp_unwrapped_ = rtp_timestamp_unwrapped;return inter_frame_delay_variation;
}评估FrameDelay = estimate[0] * (FrameSize – PreFrameSize) + estimate[1]
评估FrameDelay实现函数：
double FrameDelayVariationKalmanFilter::GetFrameDelayVariationEstimateTotal(double frame_size_variation_bytes) const {double frame_transmission_delay_ms =GetFrameDelayVariationEstimateSizeBased(frame_size_variation_bytes);double link_queuing_delay_ms = estimate_[1];return frame_transmission_delay_ms + link_queuing_delay_ms;
}3、jitter延时更新流程
三、RTT延时计算
VideoStreamBufferController::OnFrameReady函数，在判断帧有重传情况时，还会根据实际情况，在渲染帧时间里面增加RTT值。
JitterEstimator::GetJitterEstimate根据实际配置，可以在渲染时间中适当增加一定比例的RTT延时值。
四、参考
WebRTC视频接收缓冲区基于KalmanFilter的延迟模型 - 简书在WebRTC的视频处理流水线中，接收端缓冲区JitterBuffer是关键的组成部分：它负责RTP数据包乱序重排和组帧，RTP丢包重传，请求重传关键帧，估算缓冲区延迟等功能...
https://www.jianshu.com/p/bb34995c549a
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