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：

<code>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。

<code>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

<code>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