Mobility-aware DASH for cost-optimal mobile multimedia streaming services

Park, Gwangwoo; Ko, Haneul; Pack, Sangheon

doi:10.1109/infcomw.2015.7179341

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…By using the information about the environments and contexts to predict the available bandwidth, MASERATI improves the performance of DASH in terms of the playout success rate, video quality, and stability. MDASH [ 21 ] proposed a mobility-aware, dynamic, adaptive streaming over HTTP to minimize the cellular data usage through a Markov Decision Process framework, which considers the mobile network types, bandwidth, last request bitrate, and the occupation of the buffer. EnvDASH [ 22 ] introduced an environment-aware adynamic adaptive streaming over an HTTP system.…”

Section: Related Workmentioning

confidence: 99%

Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming

Zhang

Shuyan

et al. 2018

Sensors

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HTTP adaptive streaming (HAS) has become a dominated media streaming paradigm in today’s Internet, which enriches the user’s experience by matching the video quality with the dynamic network conditions. A range of HAS mechanisms have been proposed to enhance the Quality of Experience (QoE). However, existing mechanisms ignore the environmental impact in the QoE evaluation of mobile users, while the popularity of mobile video allows users to watch videos in diversified scenarios. In this paper, we propose an environment-aware HAS scheme that fully concentrates on the different criteria for evaluating video QoE under different environments. Using the advantage of the sensors in mobile phones, the scheme constructs and validates a video QoE model based on environment perception and then designs a model-driven, environment-aware HAS rate adaptation algorithm. We also evaluate the scheme with an environment-aware DASH (Dynamic Adaptive Streaming over HTTP) player in real mobile environments. Compared to the benchmark HAS mechanism, the experimental results demonstrate that our scheme can provide appropriate differentiated rate adaptation for different environments, resulting in a higher QoE.

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Section: Related Workmentioning

confidence: 99%

Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming

Zhang

Shuyan

et al. 2018

Sensors

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“…Although the effects of the interaction between TCP and the adaptation logic are not considered in this capacity model, a statistical analysis on the NYU DASH dataset (from the Crawdad 2 trace database) has shown that a Markov-Gauss process closely fits the capacity fluctuations in a real DASH streaming session, and modeling capacity variations as red noise [22] is not unrealistic. Markov capacity models are widely used in the literature, both on DASH adaptation [23][24] and TCP performance [25] studies. We built a transition matrix for all possible channel states ht and ht+1.…”

Section: Simulation Settingsmentioning

confidence: 99%

Online learning adaptation strategy for DASH clients

Chiariotti

D’Aronco

Toni

et al. 2016

Proceedings of the 7th International Conference on Multimedia Systems

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In this work, we propose an online adaptation logic for Dynamic Adaptive Streaming over HTTP (DASH) clients, where each client selects the representation that maximize the long term expected reward. The latter is defined as a combination of the decoded quality, the quality fluctuations and the rebuffering events experienced by the user during the playback. To solve this problem, we cast a Markov Decision Process (MDP) optimization for the selection of the optimal representations. System dynamics required in the MDP model are a priori unknown and are therefore learned through a Reinforcement Learning (RL) technique. The developed learning process exploits a parallel learning technique that improves the learning rate and limits sub-optimal choices, leading to a fast and yet accurate learning process that quickly converges to high and stable rewards. Therefore, the efficiency of our controller is not sacrificed for fast convergence. Simulation results show that our algorithm achieves a higher QoE than existing RL algorithms in the literature as well as heuristic solutions, as it is able to increase average QoE and reduce quality fluctuations.

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“…[17] [24][25] [43][44][45]: the average quality level, the quality variations during the video playout, the video freezes, and the startup delay. By investigating the existing QoE models[6][7] [24][25] [43][44][45][46][47][48][49], the QoE model can be written as…”

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confidence: 99%

An Ensemble Rate Adaptation Framework for Dynamic Adaptive Streaming Over HTTP

Yuan

Hou

et al. 2020

IEEE Trans. on Broadcast.

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Rate adaptation is one of the most important issues in dynamic adaptive streaming over HTTP (DASH). Due to the frequent fluctuations of the network bandwidth and complex variations of video content, it is difficult to deal with the varying network conditions and video content perfectly by using a single rate adaptation method. In this paper, we propose an ensemble rate adaptation framework for DASH, which aims to leverage the advantages of multiple methods involved in the framework to improve the quality of experience (QoE) of users. The proposed framework is simple yet very effective. Specifically, the proposed framework is composed of two modules, i.e. the method pool and method controller. In the method pool, several rate adaptation methods are integrated. At each decision time, only the method that can achieve the best QoE is chosen to determine the bitrate of the requested video segment. Besides, we also propose two strategies for switching methods, i.e., InstAnt Method Switching, and InterMittent Method Switching, for the method controller to determine which method can provide the best QoEs. Simulation results demonstrate that, the proposed framework always achieves the highest QoE for the change of channel environment and video complexity, compared with stateof-the-art rate adaptation methods.

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Mobility-aware DASH for cost-optimal mobile multimedia streaming services

Cited by 5 publications

References 1 publication

Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming

Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming

Online learning adaptation strategy for DASH clients

An Ensemble Rate Adaptation Framework for Dynamic Adaptive Streaming Over HTTP

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