A Markov Decision Model for Adaptive Scheduling of Stored Scalable Videos

Chen, Chao; Heath, Robert W.; Bovik, Alan C.; Veciana, Gustavo de

doi:10.1109/tcsvt.2013.2254896

Cited by 28 publications

(14 citation statements)

References 21 publications

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“…For the communication model, we assume that mobile device users are randomly scattered over the coverage region. For the bitrate adjustment, we revise the AMBA (Autoregressive Model Based Algorithm) [25] as the baseline algorithm to solve problem P1, termed as AR. For the spectrum allocation, we consider the generic case that the network operator aims to optimize the system throughput without the knowledge of the actual traffic demand of all the users, termed as SA.…”

Section: Data Driven Bitrate Adjustment and Channel Assignment For Msvsmentioning

confidence: 99%

Embracing social big data in wireless system design

Wen

Liu

2017

J. Commun. Inf. Netw.

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Abstract:The past decade has witnessed explosive growth in wireless big data, as well as in various big data analytics technologies. The intelligence mined from these massive datasets can be utilized to optimize wireless system design. Due to the open data policy of the mainstream OSN (Online Social Network) service providers and the pervasiveness of online social services, this paper studies how social big data can be embraced in wireless communication system design. We start with our first hand experience on crawling social big data and the principal of social-aware system design. Then we present five studies on utilizing social intelligence for system optimization, including community-aware social video distribution over cloud content delivery networks, public cloud assisted mobile social video sharing, data driven bitrate adjustment and spectrum allocation for mobile social video sharing, location-aware video streaming, and social video distribution over information-centric networking.

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Section: Data Driven Bitrate Adjustment and Channel Assignment For Msvsmentioning

confidence: 99%

Embracing social big data in wireless system design

Wen

Liu

2017

J. Commun. Inf. Netw.

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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. Two [5,10] develop a heuristic policy based on an optimization problem.…”

Section: Related Workmentioning

confidence: 99%

“…Of the ten papers, five [1,[3][4][5]16] assume that the buffer capacity on the client is unlimited, and may be filled up to the length of the video. In [12,13], no buffering is allowed at the receiver and the video transmission is performed in real time.…”

Section: Related Workmentioning

confidence: 99%

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(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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“…Although these rate adaptation techniques can effectively avoid playback interruptions due to throughput variations, the variable bitrate causes quality fluctuations, which, in turn, affect viewers' QoE. In most existing rate-adaptation algorithms such as [7,8,9,10,11,12,13], average video quality is employed as a proxy for QoE. Although the average quality tends to capture the overall video quality to some extent, This research was supported in part by Intel Inc. and Cisco Corp. under the VAWN program.…”

Section: Introductionmentioning

confidence: 99%

Adaptive video transmission with subjective quality constraints

Chen

Zhu

Veciana

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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We conducted a subjective study wherein we found that viewers' Quality of Experience (QoE) was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. Simulation results show that the proposed technique can reduce network resource consumption by 29% over conventional average-quality maximized rate-adaptation algorithms.

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A Markov Decision Model for Adaptive Scheduling of Stored Scalable Videos

Cited by 28 publications

References 21 publications

Embracing social big data in wireless system design

Embracing social big data in wireless system design

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

Adaptive video transmission with subjective quality constraints

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