Realtime Multimedia Streaming over Internet
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Realtime Multimedia Streaming over Internet
Pengjun PeiDazhen Pan
CSE 620 Fall,2001
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Overview
Wide Application Video-conference Internet telephony Streaming audio/video players
Challenges:Internet is best-effort network Packet loss Bandwidth variation Packet delay variation
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System Architecture
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Content
Video CompressionCongestion ControlError Control
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Video Compression
Various requirement: Bandwidth Delay Loss VCR like function Decoding complexity
Intra-frame redundancy & inter-frame redundancyNon-scalable coding vs Scalable coding
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Inter-frame redundancy
MPEG-2:
I frame: intra-picture P frame: predicted pictureB frame: bi-directional predicted pictureMPEG frame dependencies in an MPEG bit
stream
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Scalable Coding
FGS: fine granularity scalability(proposed to MPEG-4):
Bitplanes of enhancement DCT coeffients
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Content
Video CompressionCongestion ControlError Control
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Congestion Control
Requirements for multimedia streaming Relatively constant rate Low latency for packet delivery Small latency variance Timely delivery is more important than
complete reliability
Rate controlRate shaping
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TCP/UDP?
TCP Retransmission mechanism
intolerable delays Multiplicative decrease in case of
congestionsharp variation in visual effect
UDP Unfair to responsive TCP flows Congestion collapse
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Categories of Rate Control
Source-based rate controlSource adjusts sending rateFeedback employedCan be applied to both unicast and
multicast
Receiver-based rate controlReceiver joins layer/channel Used in multicasting scalable video
Hybrid rate control
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TCP Friendly Flows
A flow is TCP-friendly if its arrival rate does not exceed the bandwidth of a conformant TCP connection in the same circumstances.
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TCP throughput model
λ: throughput of a TCP connection
MTU: maximum transit unitRTT: Round Trip Timep: packet loss ratio
pRTT
MTU
*
*325.1
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RAP(Rate Adaptation Protocol)
Proposed by R. Rejaie 1998End-to-end architecture
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RAP
Decision function If no congestion is detected, periodically increase the
transmission rate If congestion is detected, immediately decrease the
transmission rate
Increase/Decrease algorithm: AIMDDecision frequency
Smoothed version of one RTT: most recent value of SRTT
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RAPDecision function
Mechanisms to detect loss: Timeout
SRTTi = 7/8 * SRTTi + 1/8 * SampleRTTTimeout=μ*SRTT+δ*VarRTTUse transmission history ’coz it isn’t ack-clockedBefore sending a new packet, source traverses through the transmission history and detects all timeout losses: WHILE (DepartTimei+Timeout>=CurrTime)
IF(Flagi!=Acked) THENSeqi is lost
Detect a burst of loss at once
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RAP Decision function(Continued)
Gaps in sequence number(ACK-based)
ACK Packet:Acurr:packet being acknowledged
N: the last packet before Acurr that was still missing
Alast:the last packet before N that was received Timeout mechanism as a backup for critical
scenarios such as when a burst of packets is lost
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AIMD in RAP
No-packet loss: Si = Si + α (step height) Si = PacketSize/IPGi IPGi+1 = IPGi*C/( IPGi + C ) α = Si+1 – Si = PacketSize/C
Upon packet loss: Si+1 = β*Si IPGi+1 = IPGi/β β = 0.5
IPG:inter-packet-gap
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RAP Decision Frequency
Adjust IPG once every round-trip time using most recent value of SRTTRight value of C: C must be adjusted so that in a steady state,
the number of packets transmitted per step is increased by 1.
If IPG is updated once every T seconds and we choose C = T/k, the # of packets sent during each step is increased by k every step.
RAP use k=1 to emulate the TCP window adjust
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RAP Fine-grain rate adaptation
Motivation:Make RAP more stable and responsive to transient
congestion while still performing the AIMD algorithm at a coarser granularity
Fine-grain feedback:Feedbacki=FRTTi/XRTTiFRTTi,XRTTi: short/long term exponential moving
average of RTT samples at the ith adjusting pointRTTi+1 = (1 – K)RTTi+K*SampleRTT(KXRTT=0.01 KFRTT=0.9)
Fine-grain adjustmentIPGi’ = IPGi * Feedbacki
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Simulation Result RAP
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Simulation Result(FG-RAP)
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Binomial Algo
Proposed by D. Bansal 2000
I: Increase in window as a result of receipt of one window of ACK in a RTTD:Decrease in window on detection of a loss by the senderWt: window size at time t
10:
0;/:
ltttt
kttRt
D
I
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Properties of Binomial Algo
Any l < 1 has a decrease that is in general less than a multiplicative decreaseTCP Friendly if and only ifk + l = 1 and l <= 1 for suitable α and β.Converge to fairness as long ask > =0, l >= 0, k + l > 0
10:
0;/:
ltttt
kttRt
D
I
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Ratio of throughput AIMD/Binomial
x:value of ky:TCP throughput/Binomial throuput
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SQRT(k = l = 0.5)
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SQRT vs AIMD
SQRT has less oscillatory bandwidth probing
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Categories of Rate Control
Source-based rate controlSource adjusts sending rateFeedback employedCan be applied to both unicast and
multicast
Receiver-based rate controlReceiver joins layer/channel Used in multicasting scalable video
Hybrid rate control
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Source-based Rate Control for Multicast
Unicast video distribution using multiple point-point connectionMulticast video distribution using point-to-multipoint transmission
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Single-Channel Multicast
IVS(INRIA Video-conference System): Single-channel multicast Probe-base,use AIMD Each receiver determine the network
status Source solicits network status info
through probabilistic polling to avoid feedback implosion
Compare the fraction of congested receiver with threshold
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Multiple-channel multicast
Differentiated service to receivers because each receiver can individually negotiate service parameters with the recourseBandwidth inefficiency
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Categories of Rate Control
Source-based rate controlSource adjusts sending rateFeedback employedCan be applied to both unicast and
multicast
Receiver-based rate controlReceiver joins layer/channel Used in multicasting scalable video
Hybrid rate control
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Receiver-based Rate Control
Typically applied to layered multicast video Source-based works reasonably well for
unicast Receiver-based targeted at solving
heterogeneity problem in the multicast case
Probe-based: No congestion, receiver probes for available
bandwidth by joining layer/channel When congested,receiver drops a layer
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Receiver-based Rate Control
Model-based: Based on throughput model of TCP γi:transmission rate of Layer I, L current highest
layer Starts with subscribing base layer(Layer 0), set
L=0. Obtain MTU,RTT, p for a given period, calculate
throughput λ . If λ < γ0 drop base layer and stop receiving
video Else determine L’ ,the largest integer such that
'
0
L
i i
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Categories of Rate Control
Source-based rate controlSource adjusts sending rateFeedback employedCan be applied to both unicast and
multicast
Receiver-based rate controlReceiver joins layer/channel Used in multicasting scalable video
Hybrid rate control
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Hybrid Rate Control
Targeted at multicast videoApplicable to both layered video and non-layered videoMultiple channels, sender dynamically adjusts the rate for each channelDSG(Destination Set Grouping) Multiple streams:same video info with different
rate and quality,each sent to an IP multicast group Receiver chooses a multicast group to join Source uses feedback to adjust rate for each
stream
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Rate Shaping
Adapt the rate of compressed video bit-streams to the target rate constraint
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Types of rate filter
Codec Filter: perform transcoding between different schemesFrame-dropping filter:distinguish frame types and drop frames according to importanceLayer-dropping filter:distinguish layers and drop frames according to importanceFrequency filter:discard DCT coefficient of high frequencies
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Conclusion
Binomial rate control causes less oscillation to multimedia stream
Current research separates rate control and rate shaping
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Main ReferenceDeepak Bansal and Hari Balakrishnan ,Binomial Congestion ControlProc. IEEE INFOCOM Conf., Anchorage, AK, April 2001. S.Floyd, M. Handley,J.Padhye, and J. Widmer, Equation-Based Congestion Control for Unicast Applications, Proc. ACM SIGCOMM’00, pages43-54, Stockholm,Sweden,September 2000International Organization for Standardization. Overview of the MPEG-4 Standard,December 1999Sally Floyd,Kevin Fall, Promoting the Use of End-to-End Congestion Control in the Internet IEEE/ACM Transaction on Networking,May 1999Dapeng Wu,Yiwei Thomas Hou,etc, Streaming Video over the Internet: Approaches and Directions IEEE Transaction on Circuits and System for Video Technology, Vol11,No1,February 2001Dapeng Wu, Yiwei Thomas Hou,etc, Transorting real-time video over the Internet:challenges and approaches, Proceedings of the IEEE, vl.88,no. 12, Dec.2000R.Padhye,J.Kurose,D.Towsley,and R.Koodi. A Model-based TCP-Friendly Rate Control Protocol. In Proc. IEEE NOSSDAV’99,Basking Ridge,New Jersey,June 1999R.Rejaie,M.Handely,and D.Estrin. RAP:An End-to-end Rate-based Congestion Control Mechanism for Realtime Streams in the Internet. In Proc. IEEE Infocom’99,New York,NY,March 1999