Wednesday, August 19, 2015

Session 6: A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP

Authors: Xiaoqi Yin (CMU), Abhishek Jindal (CMU), Vyas Sekar (CMU), Bruno Sinopoli (CMU)

Presenter: Xiaoqi Yin

The authors presented their to formalize the task of bitrate adaption in Internet video clients, and present Robust MPC, an improvement to existing MPC (model predictive control) systems.
Designing a bitrate controller is difficult because of the complexities of network performance such as the unreliable nature of internet performance, and the complex interactions with TCP. Open questions include the type of algorithm to use, how to balance QoE factors, and how to make it robust to various operating conditions.

QOE is linear combination of factors. Bitrate chance, rebuffer time and startup delay. Used in the online controller. Fomalized through offline QoE maximization as a mixed linear linear programming problem. Liitations of previous approaches, rate-bsed and buffer based.

Traditional MPC operates using a predictive optimization and a horizon with a sliding window to smooth out control and is used widely in many distributed control problems. In each iteration, a Mixed Integer Linear Program (MILP) is solved to compute the predicted control sequences. Unfortunately this method is not robust or fast enough for bitrate adaption, especially within a client browser.

The authors propose to solve the speed problem with their algorithm, Fast MPC, which calculates offline a lookup table of the MILP using the entire state space of model parameters. This table enables MPC control within the latency constraints of an online video player.  They evaluated this by adding Robust MPC in dash.js (an existing web video player) along with a throughput predictor. Compared against the state of the art. Improves 60% form unmodified dash.js and 15% over existing state of the art. Also 60% and 10% improvements over original and SotA respectively.

Q1. How far can you take the control theory approach: what would it look like when multiple users are competing with the same channel?
A.  Future work.

Q2. Why did you not do real experiments instead of the trace-driven evaluation in the paper?
A.     Wanted to evaluate under different QoE parameter space. Agreed real experiments would greatly benefit the work.

Q4. Does the use of the lookup table makes for an unscalable approach when calculating for different QoEs?
A. Since the lookup table is populated with the entire state space, this is not an issue.

Q5. Closer integrate with a lower level congestion control – did  you try different congestion controls with their system?
A.     The authors did not.

Q6. What does the table encode? Does it account for changes in screen size, etc?

A. Table only encapsulates bitrate.

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