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CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

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CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel
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Page 1: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Layered Coding and Networking

Ketan Mayer-Patel

Page 2: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Previously• Media Scaling

– Change encoding of media stream to adapt to changes in rate due to congestion control

• Scaling Issues– Quality vs. Bitrate tradeoffs– Feedback (what, when, how)– Stability

• Transcoding for stored media

• All of the above: unicast w/ feedback

Page 3: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Heterogeneous Receivers• Given heterogenous receivers, what is my

target rate?

S

R1

R2

R3

Page 4: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Possible solution• Multiple representations

• Issues/problems with multiple reps:– How many?

• Granularity of choice.

– How do I group receivers together?• Automatic grouping: estimate bandwidth

• Manual grouping: user knowledge

– How to switch between reps?

• Transcoding is like dynamic, run-time multi-rep.

Page 5: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Layered Representations• An encoding specifically designed to

produce multiple representations.

• Characteristics:– Additive: the more layers you get, the better the

media is.– Efficient: the sum of the layers is only slightly

greater than the best rep. at that quality.

Page 6: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Layered Solution• Use a layered representation.

• Receivers decide– Layers added and dropped to adjust to

appropriate target rate.

S

R1

R2

R3

Page 7: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Strictly Additive Layering• Layering split into:

– Base Layer– Enhancement Layers

• Each layer depends on all data in lower layers.

• Advantages: increased compression

• Disadvantages: packet loss in lower layers makes packets in higher layers useless.

Page 8: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Independent Layering• Every packet from every layer improves

quality.

• No ordering or dependency between layers.

• Advantages: Good ADU properties

• Disadvantages:– Hard to construct– Compression suffers

• Most layering schemes fall between these two extremes in some hybrid fashion.

Page 9: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Examples• Temporal layering

– Video: Odd frames vs. Even frames.• Mostly independent layers.

• Packet loss affects smoothness.

– Audio: Multiple, offset, lower-rate streams.• Inter-sample compression compromised.

• Not all combinations equally pleasing.

• Spatial layering– Each layer improves video size/resolution.– Many of the issues as in temporal.

Page 10: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Examples• SNR layering

– Layers contain different DCT coefficient ranges.

• DC and first few AC

• Low AC

• Middle AC

• High AC

– Is this an independent layering?

• Difference between SNR and spatial:– Improved resolution vs. new picture area.

Page 11: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Examples• Level of Detail

– Used for streaming geometry.– Additional layers extend previous LOD with

additional vertices and refinement.

Page 12: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

MPEG-2• Supports temporal, spatial, and SNR

• Loosely coupled, independent representation for temporal and spatial.– Well defined within the standard, but base

layers not required to be MPEG-2

• SNR is strictly defined within MPEG-2

• In general, supports a small number of layers (2-3 max) best.– Overhead and processing dominate quickly.

Page 13: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Subband Coding• Wavelets are best known subband coders.

• 3D subband coding:– Bitstream can be easily sliced along temporal,

spatial, and quality dimensions.– Highly scalable.– Can be made very independent.

• The more packets you get, the better it becomes.

– Computationally complex.– No standard schemes.

Page 14: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Quality as Throttle• Remember model:

– Recievers join layers to find correct level.– How do they know when to stop?

• Loss

– But if all packets contribute equally to quality, will receiver care about loss?

Page 15: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Quality as Throttle

Requested Bandwidth

Qua

lity

Problem also occurs with priority dropping.

Quality remains flat asloss percentage increases

because only the number of packets received matters.

Quality suffers as loss percentage increases because base layer

packets are lost in equal measurewhich makes higher layers useless.

Page 16: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Issues for adding layers• When

– Sustained performance with little to no loss.

• Hysteresis problems– When sustainable bandwidth is between the

rates of two different layers.– Probing by new members.

• Join latency– Takes a while for multicast joins to occur.– Helps to have global view of available layers.

Page 17: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Issues for dropping layers.• Usually done as a reaction to congestion.

• Everyone must do it at about the same time!

• New members probing for appropriate layer will cause “false” congestion.

• Leave latency– Same problem as join latency.

Page 18: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Source driven experiments.• Source periodically bunches together

packets to “simulate” next higher rate.– Explicit signal allows receiver to differentiate

between this and random network effects.– Average throughput over longer timescales

remains the same.– Receivers that “survive”, join next level.

• Can cause strange interactions with dynamic jitter buffer management.

• Periodic quality interruptions.

Page 19: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Source driven probing.

Time

Lay

er #

1

2

3

4

Page 20: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Brute-force methods.• Use scoping to create static zones.

– TTL-bases scoping– Administrative scoping– Requires session management support.

• No dynamic join/leave by receivers.

Page 21: CS 294-9 :: Fall 2003 Layered Coding and Networking Ketan Mayer-Patel.

CS 294-9 :: Fall 2003

Layering for unicast.• No reason why can’t be used in a unicast

context to achieve media scaling.– Complications of multicast and scaling

avoided.

• Current research:– A lot of focus on using layered representations

in conjunction with TCP-friendly rate control.• RAP

• Fine-grained scalability with TCP


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