Video Source Coding & Congestion Control. An Integrated Source Coding and Congestion Control Framework for Video Streaming in the Internet Kang-Won Lee (UIUC), Vaduvur Bhargavan(UIUC), Tae-eun Kim(UIUC), Rohit Puri(UCBERKELEY), Kannan Ramachandran(UCBERKELEY)
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An Integrated Source Coding and Congestion Control Framework for Video Streaming in the Internet
Kang-Won Lee (UIUC), Vaduvur Bhargavan(UIUC), Tae-eun Kim(UIUC), Rohit Puri(UCBERKELEY), Kannan Ramachandran(UCBERKELEY)
IEEE INFOCOM 2000
Sender Probes network for connection capacity. Increases transmission rate until packets are dropped.
Routers drop packets oblivious to the structure and content of the packets.
This network design is unacceptable for video packet, 2 reasons
The above lead to low quality in the delivered video. Hence an efficient transcoding mechanism is required that converts the MR based prioritized bitstream into a non-prioritized packet stream while ensuring graceful quality degradation when there is a packet loss.
Where E is the distortion encountered when the source is represented by 0 bits. Since quality is a 1-1 function(D(r)) of the rate r, ascertaining the quality profile d(k) of order N corresponds to finding the rate partition
The total Rate equals, i.e. if m = N, then the total Rate becomes
Given the number of packets N, each packet of size L(i.e. total rate budget R* = N * L), a bistream with
rate-distortion D(r), and the transmission profile Find R that minimizes ED subject to
(resource constraint) and
Lagrange Multipliers is used to obtain the optimal solution.
Since is a constant at optimality, for monotonically decreasing values of the absolute
value of has to be a monotonically decreasing sequence in i. Hence
2 observations are made
If the optimal solution to the original problem is the same as that to a reduced
Problem where is replaced by so that
Meaning : The above observation can be successively applied to a monotonically increasing
or flat section in the profile, and all the corresponding rate variables are equal in the
optimal solution thus reducing the dimensionality of the problem.
and generate the transmission profile by normalizing the frequencies.
where in order to maintain the long history, and is the number of instances of
in the kerneldq during the invocation of the transmission profile generator.
where is a tunable constant, is the decrease factor, and is the graded decrease factor in LIMD/H.
Comparison of congestion control schemes
Comparison of source robustness mechanisms
See Fig 10 and Fig 11(next page)
Performance in a Large network