End-to-end stochastic scheduling of scalable video overtime-varying channels

Changuel, Nesrine; Mastronarde, Nicholas; Schaar, Mihaela van der; Sayadi, Bessem; Kieffer, Michel

doi:10.1145/1873951.1874064

Cited by 7 publications

(13 citation statements)

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“…Five of these [1][2][3][4]19] use dynamic programming to derive a video delivery policy optimized for a particular objective in a specific network configuration. Of the former, two [1,3] develop a heuristic based on the optimal policy, and use the heuristic policy in their evaluation.…”

Section: Related Workmentioning

confidence: 99%

(Not) yet another policy for scalable video delivery to mobile users

Hosseini¹,

Fund²,

Panwar³

2015

Proceedings of the 7th ACM International Workshop on Mobile Video

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In this work, we provide a methodology to analyze optimal adaptation policies for scalable video delivery in mobile environments. Typically, download policies for adaptive video are tuned to very specific system settings. The aim of this work is not to propose a new policy, but instead to understand how the optimal policy changes according to the operating environment and the system characteristics of a mobile video client. Armed with this insight, we can design or adapt policies for SVC adaptive video delivery for a broader range of settings.Using a semi-Markov decision process (SMDP), we find optimal video retrieval policies for a single user, subject to different limits on buffer capacity and different wireless environments. We apply a decision tree classifier to the output of the SMDP to derive simple approximate policies for 55 scenarios and use these to derive high-level rules on the relationship between optimal download policy and the underlying channel settings. For example, we show that the optimal policy is more conservative in slowly varying channels, and becomes more greedy in fast changing channels, and that instantaneous channel state is relevant to the decision-making process only in a setting with a very limited buffer capacity and slow-varying channel.

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Section: Related Workmentioning

confidence: 99%

(Not) yet another policy for scalable video delivery to mobile users

Hosseini¹,

Fund²,

Panwar³

2015

Proceedings of the 7th ACM International Workshop on Mobile Video

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“…For slow fading channels such as those considered here, the bit error rate cannot be modeled as a constant. In [7], adaptive scheduling of scalable videos was studied using an MDP model. The reward of each frame slot was defined as a utility function of the buffer state and the transmission rate.…”

Section: B Related Workmentioning

confidence: 99%

“…Most of the existing MDP-based scheduling algorithms are based on a utility function as the optimization objective [7]- [10]. The utility function is usually written as a weighted sum of the transmission bit rate and the amount of buffered data.…”

Section: B Related Workmentioning

confidence: 99%

“…Similarly, the video packets received in each slot can also be acknowledged via a control channel with negligible delay. Therefore, similar to most of existing MDP formulations such as [7]- [10], we assume the feedback is instantaneous. For the case where feedback delay is longer than a frame slot, please refer to [16].…”

Section: A System Overviewmentioning

confidence: 99%

“…The distortion of the reference frame d ref i (s) can be recursively estimated using (3) and (7). Because the prediction structure is acyclic, the recursion terminates when the reference frame is a key picture and (3) applies.…”

Section: We Now Introduce the Definition Of D(s)mentioning

confidence: 99%

See 2 more Smart Citations

A Markov Decision Model for Adaptive Scheduling of Stored Scalable Videos

Chen

Heath

Bovik

et al. 2013

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-We propose two scheduling algorithms that seek to optimize the quality of scalably coded videos that have been stored at a video server before transmission. The first scheduling algorithm is derived from a Markov Decision Process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite state space. Based on the MDP formulation, a near-optimal scheduling policy is computed that minimize the mean square error. Using insights taken from the development of the optimal MDP-based scheduling policy, the second proposed scheduling algorithm is an online scheduling method that only requires easily measurable knowledge of the channel dynamics, and is thus viable in practice. Simulation results show that the performance of both scheduling algorithms is close to a performance upper bound also derived in this paper.

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