Towards secure and network state aware bitrate adaptation at IoT edge

Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approa...

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Vydané v:Journal of cloud computing : advances, systems and applications Ročník 9; číslo 1; s. 1 - 12
Hlavní autori: Zeng, Zeng, Che, Hang, Miao, Weiwei, Huang, Jin, Tang, Hao, Zhang, Mingxuan, Zhang, Shaqian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 13.07.2020
Springer Nature B.V
SpringerOpen
Predmet:
Adaptive algorithms
Adaptive bitrate algorithm
Algorithms
Change detection
Computer Communication Networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Configurations
Decision analysis
Digital media
Edge computing security and privacy
Impact analysis
Information Systems Applications (incl.Internet)
IoT
Performance degradation
Performance evaluation
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
Streaming media
Video transmission
Adaptive bitrate algorithm
IoT
ISSN:2192-113X, 2192-113X
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Popis
Shrnutí:Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments.
Bibliografia:ObjectType-Article-1
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ISSN:2192-113X
2192-113X
DOI:10.1186/s13677-020-00189-4