Joint source and sending rate modeling in adaptive video streaming

Colonnese, Stefania; Frossard, Pascal; Rinauro, Stefano; Rossi, Lorenzo; Scarano, Gaetano

doi:10.1016/j.image.2013.01.007

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…One possibility would be then to move (i.e., re-associate) users based on their reported CQI values. This approach however does not take into account the intrinsic variability [14] of the size of video chunks over time, which may consequently result in large inefficiencies (and service unfairness among users). To solve this issue, we propose in this paper an offloading technique that is based on the state information derived from the application layer entity in addition to using physical/data-link protocol layer status data.…”

Section: Clever Algorithmmentioning

confidence: 99%

CLEVER: A Cooperative and Cross-Layer Approach to Video Streaming in HetNets

Colonnese

Cuomo

Chiaraviglio

et al. 2018

IEEE Trans. on Mobile Comput.

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We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (µeNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of µeNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After optimally formulating the problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer as well as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE).

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Section: Clever Algorithmmentioning

confidence: 99%

CLEVER: A Cooperative and Cross-Layer Approach to Video Streaming in HetNets

Colonnese

Cuomo

Chiaraviglio

et al. 2018

IEEE Trans. on Mobile Comput.

Self Cite

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“…Therefore, the effective video streaming rate depends on both the encoding settings http://dx.doi.org/10.1016/j.adhoc.2014.07.023 1570-8705/Ó 2014 Elsevier B.V. All rights reserved. and on the end-to-end channel throughput experienced between the sender and the client [3]. Being based on the underlying reliable TCP network protocol, HAS prevents packet error and losses at the price of introducing random end-to-end packet transmission delays, which in turn may result in underflow events at the client buffer.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of sender-assisted HTTP-based video streaming in wireless ad hoc networks

et al. 2015

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“…Video traffic models have been derived for different applications in teleconferencing [2], video broadcasting [3], [4], or streaming [5]. Different stochastic models based on autoregressive processes [2], transform expanded sample (TES) processes [6], and hidden Markov models (HMMs) [7] have been considered for network design, resource allocation, buffer dimensioning, and performance evaluation [8].…”

mentioning

confidence: 99%

A Poisson Hidden Markov Model for Multiview Video Traffic

Rossi

Chakareski

Frossard

et al. 2015

IEEE/ACM Trans. Networking

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Abstract-Multiview video has recently emerged as a means to improve user experience in novel multimedia services. We propose a new stochastic model to characterize the traffic generated by a Multiview Video Coding (MVC) variable bit-rate source. To this aim, we resort to a Poisson hidden Markov model (P-HMM), in which the first (hidden) layer represents the evolution of the video activity and the second layer represents the frame sizes of the multiple encoded views. We propose a method for estimating the model parameters in long MVC sequences. We then present extensive numerical simulations assessing the model's ability to produce traffic with realistic characteristics for a general class of MVC sequences. We then extend our framework to network applications where we show that our model is able to accurately describe the sender and receiver buffers behavior in MVC transmission. Finally, we derive a model of user behavior for interactive view selection, which, in conjunction with our traffic model, is able to accurately predict actual network load in interactive multiview services.

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Joint source and sending rate modeling in adaptive video streaming

Cited by 12 publications

References 43 publications

CLEVER: A Cooperative and Cross-Layer Approach to Video Streaming in HetNets

CLEVER: A Cooperative and Cross-Layer Approach to Video Streaming in HetNets

Performance evaluation of sender-assisted HTTP-based video streaming in wireless ad hoc networks

A Poisson Hidden Markov Model for Multiview Video Traffic

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