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1 /* | |
2 * Copyright (c) 2011 The WebRTC project authors. All Rights Reserved. | |
3 * | |
4 * Use of this source code is governed by a BSD-style license | |
5 * that can be found in the LICENSE file in the root of the source | |
6 * tree. An additional intellectual property rights grant can be found | |
7 * in the file PATENTS. All contributing project authors may | |
8 * be found in the AUTHORS file in the root of the source tree. | |
9 */ | |
10 | |
11 #include "webrtc/modules/video_coding/main/source/internal_defines.h" | |
12 #include "webrtc/modules/video_coding/main/source/jitter_estimator.h" | |
13 #include "webrtc/modules/video_coding/main/source/rtt_filter.h" | |
14 #include "webrtc/system_wrappers/include/clock.h" | |
15 #include "webrtc/system_wrappers/include/field_trial.h" | |
16 | |
17 #include <assert.h> | |
18 #include <math.h> | |
19 #include <stdlib.h> | |
20 #include <string.h> | |
21 | |
22 namespace webrtc { | |
23 | |
24 enum { kStartupDelaySamples = 30 }; | |
25 enum { kFsAccuStartupSamples = 5 }; | |
26 enum { kMaxFramerateEstimate = 200 }; | |
27 | |
28 VCMJitterEstimator::VCMJitterEstimator(const Clock* clock, | |
29 int32_t vcmId, | |
30 int32_t receiverId) | |
31 : _vcmId(vcmId), | |
32 _receiverId(receiverId), | |
33 _phi(0.97), | |
34 _psi(0.9999), | |
35 _alphaCountMax(400), | |
36 _thetaLow(0.000001), | |
37 _nackLimit(3), | |
38 _numStdDevDelayOutlier(15), | |
39 _numStdDevFrameSizeOutlier(3), | |
40 _noiseStdDevs(2.33), // ~Less than 1% chance | |
41 // (look up in normal distribution table)... | |
42 _noiseStdDevOffset(30.0), // ...of getting 30 ms freezes | |
43 _rttFilter(), | |
44 fps_counter_(30), // TODO(sprang): Use an estimator with limit based on | |
45 // time, rather than number of samples. | |
46 low_rate_experiment_(kInit), | |
47 clock_(clock) { | |
48 Reset(); | |
49 } | |
50 | |
51 VCMJitterEstimator::~VCMJitterEstimator() { | |
52 } | |
53 | |
54 VCMJitterEstimator& | |
55 VCMJitterEstimator::operator=(const VCMJitterEstimator& rhs) | |
56 { | |
57 if (this != &rhs) | |
58 { | |
59 memcpy(_thetaCov, rhs._thetaCov, sizeof(_thetaCov)); | |
60 memcpy(_Qcov, rhs._Qcov, sizeof(_Qcov)); | |
61 | |
62 _vcmId = rhs._vcmId; | |
63 _receiverId = rhs._receiverId; | |
64 _avgFrameSize = rhs._avgFrameSize; | |
65 _varFrameSize = rhs._varFrameSize; | |
66 _maxFrameSize = rhs._maxFrameSize; | |
67 _fsSum = rhs._fsSum; | |
68 _fsCount = rhs._fsCount; | |
69 _lastUpdateT = rhs._lastUpdateT; | |
70 _prevEstimate = rhs._prevEstimate; | |
71 _prevFrameSize = rhs._prevFrameSize; | |
72 _avgNoise = rhs._avgNoise; | |
73 _alphaCount = rhs._alphaCount; | |
74 _filterJitterEstimate = rhs._filterJitterEstimate; | |
75 _startupCount = rhs._startupCount; | |
76 _latestNackTimestamp = rhs._latestNackTimestamp; | |
77 _nackCount = rhs._nackCount; | |
78 _rttFilter = rhs._rttFilter; | |
79 } | |
80 return *this; | |
81 } | |
82 | |
83 // Resets the JitterEstimate | |
84 void | |
85 VCMJitterEstimator::Reset() | |
86 { | |
87 _theta[0] = 1/(512e3/8); | |
88 _theta[1] = 0; | |
89 _varNoise = 4.0; | |
90 | |
91 _thetaCov[0][0] = 1e-4; | |
92 _thetaCov[1][1] = 1e2; | |
93 _thetaCov[0][1] = _thetaCov[1][0] = 0; | |
94 _Qcov[0][0] = 2.5e-10; | |
95 _Qcov[1][1] = 1e-10; | |
96 _Qcov[0][1] = _Qcov[1][0] = 0; | |
97 _avgFrameSize = 500; | |
98 _maxFrameSize = 500; | |
99 _varFrameSize = 100; | |
100 _lastUpdateT = -1; | |
101 _prevEstimate = -1.0; | |
102 _prevFrameSize = 0; | |
103 _avgNoise = 0.0; | |
104 _alphaCount = 1; | |
105 _filterJitterEstimate = 0.0; | |
106 _latestNackTimestamp = 0; | |
107 _nackCount = 0; | |
108 _fsSum = 0; | |
109 _fsCount = 0; | |
110 _startupCount = 0; | |
111 _rttFilter.Reset(); | |
112 fps_counter_.Reset(); | |
113 } | |
114 | |
115 void | |
116 VCMJitterEstimator::ResetNackCount() | |
117 { | |
118 _nackCount = 0; | |
119 } | |
120 | |
121 // Updates the estimates with the new measurements | |
122 void | |
123 VCMJitterEstimator::UpdateEstimate(int64_t frameDelayMS, uint32_t frameSizeBytes
, | |
124 bool incompleteFrame /* = false */) | |
125 { | |
126 if (frameSizeBytes == 0) | |
127 { | |
128 return; | |
129 } | |
130 int deltaFS = frameSizeBytes - _prevFrameSize; | |
131 if (_fsCount < kFsAccuStartupSamples) | |
132 { | |
133 _fsSum += frameSizeBytes; | |
134 _fsCount++; | |
135 } | |
136 else if (_fsCount == kFsAccuStartupSamples) | |
137 { | |
138 // Give the frame size filter | |
139 _avgFrameSize = static_cast<double>(_fsSum) / | |
140 static_cast<double>(_fsCount); | |
141 _fsCount++; | |
142 } | |
143 if (!incompleteFrame || frameSizeBytes > _avgFrameSize) | |
144 { | |
145 double avgFrameSize = _phi * _avgFrameSize + | |
146 (1 - _phi) * frameSizeBytes; | |
147 if (frameSizeBytes < _avgFrameSize + 2 * sqrt(_varFrameSize)) | |
148 { | |
149 // Only update the average frame size if this sample wasn't a | |
150 // key frame | |
151 _avgFrameSize = avgFrameSize; | |
152 } | |
153 // Update the variance anyway since we want to capture cases where we on
ly get | |
154 // key frames. | |
155 _varFrameSize = VCM_MAX(_phi * _varFrameSize + (1 - _phi) * | |
156 (frameSizeBytes - avgFrameSize) * | |
157 (frameSizeBytes - avgFrameSize), 1.0); | |
158 } | |
159 | |
160 // Update max frameSize estimate | |
161 _maxFrameSize = VCM_MAX(_psi * _maxFrameSize, static_cast<double>(frameSizeB
ytes)); | |
162 | |
163 if (_prevFrameSize == 0) | |
164 { | |
165 _prevFrameSize = frameSizeBytes; | |
166 return; | |
167 } | |
168 _prevFrameSize = frameSizeBytes; | |
169 | |
170 // Only update the Kalman filter if the sample is not considered | |
171 // an extreme outlier. Even if it is an extreme outlier from a | |
172 // delay point of view, if the frame size also is large the | |
173 // deviation is probably due to an incorrect line slope. | |
174 double deviation = DeviationFromExpectedDelay(frameDelayMS, deltaFS); | |
175 | |
176 if (fabs(deviation) < _numStdDevDelayOutlier * sqrt(_varNoise) || | |
177 frameSizeBytes > _avgFrameSize + _numStdDevFrameSizeOutlier * sqrt(_varF
rameSize)) | |
178 { | |
179 // Update the variance of the deviation from the | |
180 // line given by the Kalman filter | |
181 EstimateRandomJitter(deviation, incompleteFrame); | |
182 // Prevent updating with frames which have been congested by a large | |
183 // frame, and therefore arrives almost at the same time as that frame. | |
184 // This can occur when we receive a large frame (key frame) which | |
185 // has been delayed. The next frame is of normal size (delta frame), | |
186 // and thus deltaFS will be << 0. This removes all frame samples | |
187 // which arrives after a key frame. | |
188 if ((!incompleteFrame || deviation >= 0.0) && | |
189 static_cast<double>(deltaFS) > - 0.25 * _maxFrameSize) | |
190 { | |
191 // Update the Kalman filter with the new data | |
192 KalmanEstimateChannel(frameDelayMS, deltaFS); | |
193 } | |
194 } | |
195 else | |
196 { | |
197 int nStdDev = (deviation >= 0) ? _numStdDevDelayOutlier : -_numStdDevDel
ayOutlier; | |
198 EstimateRandomJitter(nStdDev * sqrt(_varNoise), incompleteFrame); | |
199 } | |
200 // Post process the total estimated jitter | |
201 if (_startupCount >= kStartupDelaySamples) | |
202 { | |
203 PostProcessEstimate(); | |
204 } | |
205 else | |
206 { | |
207 _startupCount++; | |
208 } | |
209 } | |
210 | |
211 // Updates the nack/packet ratio | |
212 void | |
213 VCMJitterEstimator::FrameNacked() | |
214 { | |
215 // Wait until _nackLimit retransmissions has been received, | |
216 // then always add ~1 RTT delay. | |
217 // TODO(holmer): Should we ever remove the additional delay if the | |
218 // the packet losses seem to have stopped? We could for instance scale | |
219 // the number of RTTs to add with the amount of retransmissions in a given | |
220 // time interval, or similar. | |
221 if (_nackCount < _nackLimit) | |
222 { | |
223 _nackCount++; | |
224 } | |
225 } | |
226 | |
227 // Updates Kalman estimate of the channel | |
228 // The caller is expected to sanity check the inputs. | |
229 void | |
230 VCMJitterEstimator::KalmanEstimateChannel(int64_t frameDelayMS, | |
231 int32_t deltaFSBytes) | |
232 { | |
233 double Mh[2]; | |
234 double hMh_sigma; | |
235 double kalmanGain[2]; | |
236 double measureRes; | |
237 double t00, t01; | |
238 | |
239 // Kalman filtering | |
240 | |
241 // Prediction | |
242 // M = M + Q | |
243 _thetaCov[0][0] += _Qcov[0][0]; | |
244 _thetaCov[0][1] += _Qcov[0][1]; | |
245 _thetaCov[1][0] += _Qcov[1][0]; | |
246 _thetaCov[1][1] += _Qcov[1][1]; | |
247 | |
248 // Kalman gain | |
249 // K = M*h'/(sigma2n + h*M*h') = M*h'/(1 + h*M*h') | |
250 // h = [dFS 1] | |
251 // Mh = M*h' | |
252 // hMh_sigma = h*M*h' + R | |
253 Mh[0] = _thetaCov[0][0] * deltaFSBytes + _thetaCov[0][1]; | |
254 Mh[1] = _thetaCov[1][0] * deltaFSBytes + _thetaCov[1][1]; | |
255 // sigma weights measurements with a small deltaFS as noisy and | |
256 // measurements with large deltaFS as good | |
257 if (_maxFrameSize < 1.0) | |
258 { | |
259 return; | |
260 } | |
261 double sigma = (300.0 * exp(-fabs(static_cast<double>(deltaFSBytes)) / | |
262 (1e0 * _maxFrameSize)) + 1) * sqrt(_varNoise); | |
263 if (sigma < 1.0) | |
264 { | |
265 sigma = 1.0; | |
266 } | |
267 hMh_sigma = deltaFSBytes * Mh[0] + Mh[1] + sigma; | |
268 if ((hMh_sigma < 1e-9 && hMh_sigma >= 0) || (hMh_sigma > -1e-9 && hMh_sigma
<= 0)) | |
269 { | |
270 assert(false); | |
271 return; | |
272 } | |
273 kalmanGain[0] = Mh[0] / hMh_sigma; | |
274 kalmanGain[1] = Mh[1] / hMh_sigma; | |
275 | |
276 // Correction | |
277 // theta = theta + K*(dT - h*theta) | |
278 measureRes = frameDelayMS - (deltaFSBytes * _theta[0] + _theta[1]); | |
279 _theta[0] += kalmanGain[0] * measureRes; | |
280 _theta[1] += kalmanGain[1] * measureRes; | |
281 | |
282 if (_theta[0] < _thetaLow) | |
283 { | |
284 _theta[0] = _thetaLow; | |
285 } | |
286 | |
287 // M = (I - K*h)*M | |
288 t00 = _thetaCov[0][0]; | |
289 t01 = _thetaCov[0][1]; | |
290 _thetaCov[0][0] = (1 - kalmanGain[0] * deltaFSBytes) * t00 - | |
291 kalmanGain[0] * _thetaCov[1][0]; | |
292 _thetaCov[0][1] = (1 - kalmanGain[0] * deltaFSBytes) * t01 - | |
293 kalmanGain[0] * _thetaCov[1][1]; | |
294 _thetaCov[1][0] = _thetaCov[1][0] * (1 - kalmanGain[1]) - | |
295 kalmanGain[1] * deltaFSBytes * t00; | |
296 _thetaCov[1][1] = _thetaCov[1][1] * (1 - kalmanGain[1]) - | |
297 kalmanGain[1] * deltaFSBytes * t01; | |
298 | |
299 // Covariance matrix, must be positive semi-definite | |
300 assert(_thetaCov[0][0] + _thetaCov[1][1] >= 0 && | |
301 _thetaCov[0][0] * _thetaCov[1][1] - _thetaCov[0][1] * _thetaCov[1][0]
>= 0 && | |
302 _thetaCov[0][0] >= 0); | |
303 } | |
304 | |
305 // Calculate difference in delay between a sample and the | |
306 // expected delay estimated by the Kalman filter | |
307 double | |
308 VCMJitterEstimator::DeviationFromExpectedDelay(int64_t frameDelayMS, | |
309 int32_t deltaFSBytes) const | |
310 { | |
311 return frameDelayMS - (_theta[0] * deltaFSBytes + _theta[1]); | |
312 } | |
313 | |
314 // Estimates the random jitter by calculating the variance of the | |
315 // sample distance from the line given by theta. | |
316 void VCMJitterEstimator::EstimateRandomJitter(double d_dT, | |
317 bool incompleteFrame) { | |
318 uint64_t now = clock_->TimeInMicroseconds(); | |
319 if (_lastUpdateT != -1) { | |
320 fps_counter_.AddSample(now - _lastUpdateT); | |
321 } | |
322 _lastUpdateT = now; | |
323 | |
324 if (_alphaCount == 0) { | |
325 assert(false); | |
326 return; | |
327 } | |
328 double alpha = | |
329 static_cast<double>(_alphaCount - 1) / static_cast<double>(_alphaCount); | |
330 _alphaCount++; | |
331 if (_alphaCount > _alphaCountMax) | |
332 _alphaCount = _alphaCountMax; | |
333 | |
334 if (LowRateExperimentEnabled()) { | |
335 // In order to avoid a low frame rate stream to react slower to changes, | |
336 // scale the alpha weight relative a 30 fps stream. | |
337 double fps = GetFrameRate(); | |
338 if (fps > 0.0) { | |
339 double rate_scale = 30.0 / fps; | |
340 // At startup, there can be a lot of noise in the fps estimate. | |
341 // Interpolate rate_scale linearly, from 1.0 at sample #1, to 30.0 / fps | |
342 // at sample #kStartupDelaySamples. | |
343 if (_alphaCount < kStartupDelaySamples) { | |
344 rate_scale = | |
345 (_alphaCount * rate_scale + (kStartupDelaySamples - _alphaCount)) / | |
346 kStartupDelaySamples; | |
347 } | |
348 alpha = pow(alpha, rate_scale); | |
349 } | |
350 } | |
351 | |
352 double avgNoise = alpha * _avgNoise + (1 - alpha) * d_dT; | |
353 double varNoise = | |
354 alpha * _varNoise + (1 - alpha) * (d_dT - _avgNoise) * (d_dT - _avgNoise); | |
355 if (!incompleteFrame || varNoise > _varNoise) { | |
356 _avgNoise = avgNoise; | |
357 _varNoise = varNoise; | |
358 } | |
359 if (_varNoise < 1.0) { | |
360 // The variance should never be zero, since we might get | |
361 // stuck and consider all samples as outliers. | |
362 _varNoise = 1.0; | |
363 } | |
364 } | |
365 | |
366 double | |
367 VCMJitterEstimator::NoiseThreshold() const | |
368 { | |
369 double noiseThreshold = _noiseStdDevs * sqrt(_varNoise) - _noiseStdDevOffset
; | |
370 if (noiseThreshold < 1.0) | |
371 { | |
372 noiseThreshold = 1.0; | |
373 } | |
374 return noiseThreshold; | |
375 } | |
376 | |
377 // Calculates the current jitter estimate from the filtered estimates | |
378 double | |
379 VCMJitterEstimator::CalculateEstimate() | |
380 { | |
381 double ret = _theta[0] * (_maxFrameSize - _avgFrameSize) + NoiseThreshold(); | |
382 | |
383 // A very low estimate (or negative) is neglected | |
384 if (ret < 1.0) { | |
385 if (_prevEstimate <= 0.01) | |
386 { | |
387 ret = 1.0; | |
388 } | |
389 else | |
390 { | |
391 ret = _prevEstimate; | |
392 } | |
393 } | |
394 if (ret > 10000.0) // Sanity | |
395 { | |
396 ret = 10000.0; | |
397 } | |
398 _prevEstimate = ret; | |
399 return ret; | |
400 } | |
401 | |
402 void | |
403 VCMJitterEstimator::PostProcessEstimate() | |
404 { | |
405 _filterJitterEstimate = CalculateEstimate(); | |
406 } | |
407 | |
408 void | |
409 VCMJitterEstimator::UpdateRtt(int64_t rttMs) | |
410 { | |
411 _rttFilter.Update(rttMs); | |
412 } | |
413 | |
414 void | |
415 VCMJitterEstimator::UpdateMaxFrameSize(uint32_t frameSizeBytes) | |
416 { | |
417 if (_maxFrameSize < frameSizeBytes) | |
418 { | |
419 _maxFrameSize = frameSizeBytes; | |
420 } | |
421 } | |
422 | |
423 // Returns the current filtered estimate if available, | |
424 // otherwise tries to calculate an estimate. | |
425 int VCMJitterEstimator::GetJitterEstimate(double rttMultiplier) { | |
426 double jitterMS = CalculateEstimate() + OPERATING_SYSTEM_JITTER; | |
427 if (_filterJitterEstimate > jitterMS) | |
428 jitterMS = _filterJitterEstimate; | |
429 if (_nackCount >= _nackLimit) | |
430 jitterMS += _rttFilter.RttMs() * rttMultiplier; | |
431 | |
432 if (LowRateExperimentEnabled()) { | |
433 static const double kJitterScaleLowThreshold = 5.0; | |
434 static const double kJitterScaleHighThreshold = 10.0; | |
435 double fps = GetFrameRate(); | |
436 // Ignore jitter for very low fps streams. | |
437 if (fps < kJitterScaleLowThreshold) { | |
438 if (fps == 0.0) { | |
439 return jitterMS; | |
440 } | |
441 return 0; | |
442 } | |
443 | |
444 // Semi-low frame rate; scale by factor linearly interpolated from 0.0 at | |
445 // kJitterScaleLowThreshold to 1.0 at kJitterScaleHighThreshold. | |
446 if (fps < kJitterScaleHighThreshold) { | |
447 jitterMS = | |
448 (1.0 / (kJitterScaleHighThreshold - kJitterScaleLowThreshold)) * | |
449 (fps - kJitterScaleLowThreshold) * jitterMS; | |
450 } | |
451 } | |
452 | |
453 return static_cast<uint32_t>(jitterMS + 0.5); | |
454 } | |
455 | |
456 bool VCMJitterEstimator::LowRateExperimentEnabled() { | |
457 if (low_rate_experiment_ == kInit) { | |
458 std::string group = | |
459 webrtc::field_trial::FindFullName("WebRTC-ReducedJitterDelay"); | |
460 if (group == "Disabled") { | |
461 low_rate_experiment_ = kDisabled; | |
462 } else { | |
463 low_rate_experiment_ = kEnabled; | |
464 } | |
465 } | |
466 return low_rate_experiment_ == kEnabled ? true : false; | |
467 } | |
468 | |
469 double VCMJitterEstimator::GetFrameRate() const { | |
470 if (fps_counter_.count() == 0) | |
471 return 0; | |
472 | |
473 double fps = 1000000.0 / fps_counter_.ComputeMean(); | |
474 // Sanity check. | |
475 assert(fps >= 0.0); | |
476 if (fps > kMaxFramerateEstimate) { | |
477 fps = kMaxFramerateEstimate; | |
478 } | |
479 return fps; | |
480 } | |
481 | |
482 } | |
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