Toward a Principled Framework to Design Dynamic Adaptive Streaming - - PowerPoint PPT Presentation

Toward a Principled Framework to Design Dynamic Adaptive Streaming Alg lgorithms over HTTP

Xiaoqi Yin, Vyas Sekar, Bruno Sinopoli

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Design Dynamic Adaptive Streaming (D (DASH) algorithms is is cri ritical for better QoE

Video Source Adaptive Video Players

B/W Bitrate Time

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Internet

QoE

Vid ideo pla layer model

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Internet B/W Predictor Bitrate Controller HTTP

GET Chunk Buffer

• ccupancy

Video Player Buffer End User

Bitrate Predicted B/W

Vid ideo pla layer adaptation is is hard

• Hard to predict bandwidth
• It is a stochastic variable difficult to estimate
• Interaction with TCP
• Makes bandwidth estimation even harder
• Various factors can impact QoE
• Often in conflict, e.g., high quality vs. few stalls
• Discrete feedback and control
• Discrete bitrate levels, change bitrate only in discrete time

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Ple lenty of f algorithms, , lit little cla larity

• 50+ papers in the past 5 years

Rate-based Buffer-based

Controller Bitrate k Estimated B/W Buffer Controller Bitrate k B/W “Match bitrate with bandwidth” “Control buffer to certain level”

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Buffer

Are they optimal?

 Broader design space is possible

Rate based Buffer-based

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Bitrate k B/W Buffer A1 A2 A3 ?

Several point solutions! Objective they (should) optimize?

i.e., Buffering vs. switching vs. bitrate

Sensitivity to operating regimes

e.g., When is A1 better than A2?

We need a systematic fr framework!

Design space

• f all algorithms

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Choose optimal bitrate for all chunks in a video R1, R2, … , RK To maximize QoE: q(avg bitrate, bitrate switches, rebuffer time) Subject to:

• Buffer occupancy dynamics:

Bufk+1 = g( Bufk, B/W, Rk )

• Available bandwidth

Formally Defined Broader Design Space Sensitivity To Operating Parameters

Stochastic optimal control fr framework

Online controller design: Rk = f (Bufk, Predicted B/W)

Known precisely Predicted with error

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Choose optimal bitrate for all chunks in a video R1, R2, … , RK To maximize QoE: q(avg bitrate, bitrate switches, rebuffer time) Subject to:

• Buffer occupancy dynamics:

Bufk+1 = g( Bufk, B/W, Rk )

• Available bandwidth

Algorithm via Model Predictive Control (M (MPC)

Online controller design: Rk = f (Bufk, Predicted B/W)

Known precisely Predicted with error

Model predictive control

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• 1. Moving

horizon: At step k, plan for next N chunks (k to k+N)

• 2. Predict:

Predict B/W within the horizon k to k+N

• 3. Control: Select

bitrates to maximize QoE within the horizon, apply 1st bitrate Rk

• Use both bandwidth

and buffer information

• Smoothing out

prediction error at each step

• Embed the control
• bjective directly

into controller

Main result: MPC > BB > RB

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Does prediction error matter?

MPC > BB BB > MPC

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Summary of f oth ther in insights

• All algorithms benefit from finer-grained bitrate sets
• MPC/BB can achieve near-zero buffering while RB cannot
• MPC do better on avoiding bitrate variations

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Dis iscussion & Li Limitations

• Full-spectrum sensitivity analysis
• Bandwidth estimation, interaction with TCP
• Characterizing bandwidth stability/predictability
• Multi-player interactions
• Computational complexity

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Conclusions

• Lots of confusion in video player design
• What is the objective? How to compare algorithms?
• How sensitive is the solution?
• Use control theory to bring rigor to DASH design
• MPC outperforms BB and RB in certain conditions
• Future work: Bring control-theoretic framework to

practice

Thank you!

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