流媒体学习之路(WebRTC)——Pacer与GCC（5）

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  在讲具体内容之前插一句嘴，从GCC分析（3）开始，我们将针对GCC的实现细节去分析它设计的原理，让我们理解这些类存在的意义，不再带大家去串具体的流程了。

一、PacingController

1.1 背景介绍

  Pacer（Packet Pacing）的作用是在传输数据时能平滑的发送出去，减少对网络冲击和抖动的产生，提高通信质量。在一次数据传输中，如果所有包几乎同时发送，网络就可能会遭遇到冲击，这就可能导致网络拥塞，数据包丢失等问题。为了避免这样的问题，需要通过一个定时器均匀分散发送数据包。
  特别是在音视频传输中，PACER更是非常重要的一部分。因为音视频的传输对于网络的稳定性和实时性要求非常高，任何形式的网络抖动或者丢包都会造成音视频的卡顿，延迟等问题。所以在WebRTC中使用Pacer，就是为了使音视频传输更加平滑，减少由于网络抖动造成的影响，从而达到提高实时音视频通信质量的目的。

  提到WebRTC的Pacer就需要讲述它码率控制的逻辑：

  从GCC输出的码率会设置给编码器以及pacer。pacer并不是完全严格设置多少就发多少，而是留有2.5倍的空间去发送。真正控制发送码率的则是输出给编码器的部分，期望控制编码器的输出码率。同时，pacer还对所有数据设置了优先级，优先级如下：

int GetPriorityForType(RtpPacketToSend::Type type) {// Lower number takes priority over higher.switch (type) {case RtpPacketToSend::Type::kAudio:// Audio is always prioritized over other packet types.return kFirstPriority + 1;case RtpPacketToSend::Type::kRetransmission:// Send retransmissions before new media.return kFirstPriority + 2;case RtpPacketToSend::Type::kVideo:case RtpPacketToSend::Type::kForwardErrorCorrection:// Video has "normal" priority, in the old speak.// Send redundancy concurrently to video. If it is delayed it might have a// lower chance of being useful.return kFirstPriority + 3;case RtpPacketToSend::Type::kPadding:// Packets that are in themselves likely useless, only sent to keep the// BWE high.return kFirstPriority + 4;}
}

  Pacer之所设计成这样，是因为我们向编码器设置码率之后想要保证丝滑清晰的画面，不可能完全控制输出码率，有时候画面复杂码率就大一些，画面简单码率就小一些。所以Pacer为了保证延迟预留了2.5倍的发送空间，也就是说真正控制码率的位置其实是编码器的输出。

1.2 代码

  接下来我看看看pacer的核心代码——PacingController。这个类包含了优先级设置以及发送的逻辑，前面提到了优先级的内容下面只介绍发送逻辑：

void PacingController::ProcessPackets() {Timestamp now = CurrentTime(); // 当前时间TimeDelta elapsed_time = UpdateTimeAndGetElapsed(now); // 与上次process的间隔// 发送保活，每500ms发送一个padding包，一旦发送的数据大于拥塞窗口则不发送if (ShouldSendKeepalive(now)) {DataSize keepalive_data_sent = DataSize::Zero();// 产生padding包std::vector<std::unique_ptr<RtpPacketToSend>> keepalive_packets =packet_sender_->GeneratePadding(DataSize::bytes(1));for (auto& packet : keepalive_packets) {keepalive_data_sent +=DataSize::bytes(packet->payload_size() + packet->padding_size());packet_sender_->SendRtpPacket(std::move(packet), PacedPacketInfo());}OnPaddingSent(keepalive_data_sent);}// 处于暂停直接返回if (paused_)return;// 进入发送间隔开始计算if (elapsed_time > TimeDelta::Zero()) {DataRate target_rate = pacing_bitrate_;DataSize queue_size_data = packet_queue_.Size();// 队列中有数据才能发送if (queue_size_data > DataSize::Zero()) {// Assuming equal size packets and input/output rate, the average packet// has avg_time_left_ms left to get queue_size_bytes out of the queue, if// time constraint shall be met. Determine bitrate needed for that.// packet_queue_.UpdateQueueTime(CurrentTime());if (drain_large_queues_) {// 平均发送时间 = 最大队列时长（2s）- 平均排队时间TimeDelta avg_time_left =std::max(TimeDelta::ms(1),queue_time_limit - packet_queue_.AverageQueueTime());DataRate min_rate_needed = queue_size_data / avg_time_left;// 最发送码率大于目标码率，则目标码率等于最小需求码率if (min_rate_needed > target_rate) {target_rate = min_rate_needed;RTC_LOG(LS_VERBOSE) << "bwe:large_pacing_queue pacing_rate_kbps="<< target_rate.kbps();}}}// 设置媒体桶media_budget_.set_target_rate_kbps(target_rate.kbps());UpdateBudgetWithElapsedTime(elapsed_time);}bool first_packet_in_probe = false;bool is_probing = prober_.IsProbing();PacedPacketInfo pacing_info;absl::optional<DataSize> recommended_probe_size;// 正在探测则获取探测数据信息if (is_probing) {pacing_info = prober_.CurrentCluster();first_packet_in_probe = pacing_info.probe_cluster_bytes_sent == 0;recommended_probe_size = DataSize::bytes(prober_.RecommendedMinProbeSize());}DataSize data_sent = DataSize::Zero();// The paused state is checked in the loop since it leaves the critical// section allowing the paused state to be changed from other code.// while (!paused_) {if (small_first_probe_packet_ && first_packet_in_probe) {// If first packet in probe, insert a small padding packet so we have a// more reliable start window for the rate estimation.// 产生padding包auto padding = packet_sender_->GeneratePadding(DataSize::bytes(1));// If no RTP modules sending media are registered, we may not get a// padding packet back.if (!padding.empty()) {// Insert with high priority so larger media packets don't preempt it.EnqueuePacketInternal(std::move(padding[0]), kFirstPriority);// We should never get more than one padding packets with a requested// size of 1 byte.RTC_DCHECK_EQ(padding.size(), 1u);}first_packet_in_probe = false;}// 获取待发送包auto* packet = GetPendingPacket(pacing_info);// 一旦产生不了数据，证明队列为空，则放入padding数据if (packet == nullptr) {// No packet available to send, check if we should send padding.DataSize padding_to_add = PaddingToAdd(recommended_probe_size, data_sent);if (padding_to_add > DataSize::Zero()) {std::vector<std::unique_ptr<RtpPacketToSend>> padding_packets =packet_sender_->GeneratePadding(padding_to_add);if (padding_packets.empty()) {// No padding packets were generated, quite send loop.break;}for (auto& packet : padding_packets) {EnqueuePacket(std::move(packet));}// Continue loop to send the padding that was just added.continue;}// Can't fetch new packet and no padding to send, exit send loop.break;}// 发送数据std::unique_ptr<RtpPacketToSend> rtp_packet = packet->ReleasePacket();RTC_DCHECK(rtp_packet);packet_sender_->SendRtpPacket(std::move(rtp_packet), pacing_info);data_sent += packet->size();// Send succeeded, remove it from the queue.OnPacketSent(packet);if (recommended_probe_size && data_sent > *recommended_probe_size)break;}if (is_probing) {probing_send_failure_ = data_sent == DataSize::Zero();if (!probing_send_failure_) {prober_.ProbeSent(CurrentTime().ms(), data_sent.bytes());}}
}RoundRobinPacketQueue::QueuedPacket* PacingController::GetPendingPacket(const PacedPacketInfo& pacing_info) {if (packet_queue_.Empty()) {return nullptr;}// Since we need to release the lock in order to send, we first pop the// element from the priority queue but keep it in storage, so that we can// reinsert it if send fails.// 取出第一个包RoundRobinPacketQueue::QueuedPacket* packet = packet_queue_.BeginPop();bool audio_packet = packet->type() == RtpPacketToSend::Type::kAudio;bool apply_pacing = !audio_packet || pace_audio_;// 如果处于拥塞状态或者剩余数据为0则取消弹出if (apply_pacing && (Congested() || (media_budget_.bytes_remaining() == 0 &&pacing_info.probe_cluster_id ==PacedPacketInfo::kNotAProbe))) {packet_queue_.CancelPop();return nullptr;}return packet;
}

二、IntervalBudget

2.1 背景

  PacingController上述用到了IntervalBudget这个类，这个类用于做数据统计和预估。并且它作为一个抽象预估类，并不会真正的存数据，只是做了数据统计，每次排出数据后都按时间更新一次桶的容量，发送时则会把已发送的数据更新到桶数据中。

2.2 代码

  头文件：

class IntervalBudget {public:explicit IntervalBudget(int initial_target_rate_kbps);IntervalBudget(int initial_target_rate_kbps, bool can_build_up_underuse);void set_target_rate_kbps(int target_rate_kbps);// TODO(tschumim): Unify IncreaseBudget and UseBudget to one function.void IncreaseBudget(int64_t delta_time_ms);void UseBudget(size_t bytes);size_t bytes_remaining() const;double budget_ratio() const;int target_rate_kbps() const;private:int target_rate_kbps_;int64_t max_bytes_in_budget_;int64_t bytes_remaining_;bool can_build_up_underuse_;
};

  CPP文件：

constexpr int64_t kWindowMs = 500;
}IntervalBudget::IntervalBudget(int initial_target_rate_kbps): IntervalBudget(initial_target_rate_kbps, false) {}IntervalBudget::IntervalBudget(int initial_target_rate_kbps,bool can_build_up_underuse): bytes_remaining_(0), can_build_up_underuse_(can_build_up_underuse) {set_target_rate_kbps(initial_target_rate_kbps);
}void IntervalBudget::set_target_rate_kbps(int target_rate_kbps) {target_rate_kbps_ = target_rate_kbps;// 默认按500ms计算最大桶码率max_bytes_in_budget_ = (kWindowMs * target_rate_kbps_) / 8;// 计算剩余码率bytes_remaining_ = std::min(std::max(-max_bytes_in_budget_, bytes_remaining_),max_bytes_in_budget_);
}void IntervalBudget::IncreaseBudget(int64_t delta_time_ms) {// 按时换算桶的码率int64_t bytes = target_rate_kbps_ * delta_time_ms / 8;if (bytes_remaining_ < 0 || can_build_up_underuse_) {// We overused last interval, compensate this interval.// 把当前的码率加上bytes_remaining_ = std::min(bytes_remaining_ + bytes, max_bytes_in_budget_);} else {// If we underused last interval we can't use it this interval.// 一旦剩余码率为负则重新使用新计算的码率bytes_remaining_ = std::min(bytes, max_bytes_in_budget_);}
}void IntervalBudget::UseBudget(size_t bytes) {// 把使用的数据进行统计bytes_remaining_ = std::max(bytes_remaining_ - static_cast<int>(bytes),-max_bytes_in_budget_);
}size_t IntervalBudget::bytes_remaining() const {return rtc::saturated_cast<size_t>(std::max<int64_t>(0, bytes_remaining_));
}double IntervalBudget::budget_ratio() const {if (max_bytes_in_budget_ == 0)return 0.0;return static_cast<double>(bytes_remaining_) / max_bytes_in_budget_;
}int IntervalBudget::target_rate_kbps() const {return target_rate_kbps_;
}

三、PacedSender

  上述的PacingController把具体的发送数据进行具体的计算，WebRTC把发送的逻辑和控制逻辑抽离了出来，其实PacingSender在构造时创建了PacingController并传入了this指针。因此对于PacingController来说PacingSender作为控制器在内部进行了回调。

  其他的函数我们不做具体的描述，只介绍定时函数：

int64_t PacedSender::TimeUntilNextProcess() {rtc::CritScope cs(&critsect_);// When paused we wake up every 500 ms to send a padding packet to ensure// we won't get stuck in the paused state due to no feedback being received.// 从controller中获取间隔TimeDelta elapsed_time = pacing_controller_.TimeElapsedSinceLastProcess();if (pacing_controller_.IsPaused()) {// 最大间隔为500msreturn std::max(PacingController::kPausedProcessInterval - elapsed_time,TimeDelta::Zero()).ms();}auto next_probe = pacing_controller_.TimeUntilNextProbe();if (next_probe) {return next_probe->ms();}const TimeDelta min_packet_limit = TimeDelta::ms(5);return std::max(min_packet_limit - elapsed_time, TimeDelta::Zero()).ms();
}

四、总结

  本文介绍了Pacer相关的内容，但我们的目的是通过Pacer去理解GCC的逻辑，在经过多个版本的迭代，Pacer与GCC的配合已经非常娴熟，同时耦合也是非常严重的：

1. 每次Pacer的溢出发送，都需要GCC兜底（GCC的灵敏可以有效地检测到网络的排队，任何一个溢出的数据都能快速的下调码率，在遇到瓶颈带宽的时候出现了明显的锯齿状发送曲线）；
   

2. 码率不足与拥塞探测的矛盾（编码器的输出往往会收到一定的限制不可能无线地上涨，在当今环境下很难探测到带宽瓶颈。Pacer的做法是提供Padding的数据作为补充探测，但大部分厂商为了避免流量过度消耗，就把探测的逻辑关闭了。在这方面来看，Pacer真是没有完全听GCC的话）；

  也正是因为这样，WebRTC的Pacer是GCC的Pacer其他的拥塞算法来了，估计都水土不服，参考BBR被移除可知。