Application of active queue management for real-time adaptive video streaming

Morais, Wladimir Gonçalves de; Santos, Carlos Eduardo Maffini; Pedroso, Carlos Marcelo

doi:10.1007/s11235-021-00848-0

Cited by 18 publications

(7 citation statements)

References 32 publications

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“…The risk of data traffic congestion has been recognized, and much research has been done on improving queue management, network congestion detection, congestion control policies, and technologies in general to help reduce internet latency (Briscoe et al., 2016; de Morais et al., 2022; Mishra et al., 2018; Turkovic et al., 2018; Verma & Kumar, 2020; Xia et al., 2021). However, while scientists and engineers have long been concerned with how to avoid and resolve data traffic congestion, smart cities and the IoT pose new technical challenges due to the enormous heterogeneity of devices which require a multitude of different management policies for data transmission (Mishra et al., 2018, p. 445).…”

Section: Historical and Relational Analogy: Massification And Congest...mentioning

confidence: 99%

Smart cities at risk: Systemic risk drivers in the blind spot of long‐term governance

Ottenburger

Ufer

2023

Risk Analysis

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In this article, we analyze “digital massification” in smart cities, that is, an ever‐growing number of market participants, consumers, and Internet of Things devices with simultaneous accommodation of users to increasing disturbances and inconveniences due to data congestion—as a driver for systemic risk. We argue that digital massification phenomena largely escape societal awareness due to their protracted evolution and are therefore still in the blind spot of long‐term governance. Our analysis makes methodological use of historical and relational analogy, and we introduce and elaborate concepts and terms that allow us to discuss the evolutionary nature of massification, that is, the foreseeable increasing probability of the occurrence of trigger events. Using the analogy to the history of road traffic congestion, we deduce that digital massification will most likely lead to a future “risk transition” where tolerated disturbances and inconveniences of the present will turn into systemic impacts. This insight calls for heightened sensitivity in governance to massification phenomena to ensure the long‐term resilience of smart cities.

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Section: Historical and Relational Analogy: Massification And Congest...mentioning

confidence: 99%

Smart cities at risk: Systemic risk drivers in the blind spot of long‐term governance

Ottenburger

Ufer

2023

Risk Analysis

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“…Real-time video streaming techniques also differ from non-real-time in their basic operation, but one of the most important differences is that real-time video streaming is much more sensitive to delay, jitter, and packet loss [14][15][16][17]. To provide the best possible user experience, developers use various adaptive streaming techniques, such as Dynamic Adaptive Streaming over HTTP (DASH) [18] or Active Queue Management (AQM) [19] [20]. Today, DASH-based systems are the basic solution for nonreal-time and real-time video streaming systems and it is a common solution to use DASH systems in combination with AQM solutions.…”

Section: B Video Streamingmentioning

confidence: 99%

Low latency 3D image streaming in medicine

Vincze,

Biricz,

Benhamida

et al. 2023

Syst. Theor. Control Comput. J.

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3D visualization has been present in medicine for several decades, just think of the CT (Computer Tomography) imaging modality, which is capable of creating 3D images of the examined object by determining the radiation attenuation coefficient. In addition to CT equipment, there are many solutions capable of collecting and displaying various 3D data (MRI [Magnetic Resonance Imaging], PET [Positron Emission Tomography], OCT [Optical Computer Tomography]). In digital pathology, 3D visualization is not nearly as widespread as the previously mentioned solutions. One of the main reasons for this is the size of the digitized pathological samples and the hardware resources required for their 3D visualization. In the paper, we present a client-server architecture that can provide a solution to the existing hardware limitations during the 3D visualization of digitized pathological samples. In the paper, we present the basic structure of our solution, several main functionalities, and the testing of the server software we developed.

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“…In summary, AQM methods were proposed to minimize the loss and dropping rate [50]. Existing AQM methods can be classified into non-adaptive and adaptive categories.…”

Section: The Related Workmentioning

confidence: 99%

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Alshahrani,

Abu-Shareha,

Shambour

et al. 2023

CMC

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Active queue management (AQM) methods manage the queued packets at the router buffer, prevent buffer congestion, and stabilize the network performance. The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping. This paper proposes a fully adaptive active queue management (AAQM) method to maintain stable network performance, avoid congestion and packet loss, and eliminate unnecessary packet dropping. The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status. The proposed AAQM method adapts to single and multiclass traffic models. Extensive simulation results over two types of traffic showed that the proposed method achieved the best results compared to the existing methods, including Random Early Detection (RED), BLUE, Effective RED (ERED), Fuzzy RED (FRED), Fuzzy Gentle RED (FGRED), and Fuzzy BLUE (FBLUE). The proposed and compared methods achieved similar results with low or moderate traffic load. However, under high traffic load, the proposed AAQM method achieved the best rate of zero loss, similar to BLUE, compared to 0.01 for RED, 0.27 for ERED, 0.04 for FRED, 0.12 for FGRED, and 0.44 for FBLUE. For throughput, the proposed AAQM method achieved the highest rate of 0.54, surpassing the BLUE method's throughput of 0.43. For delay, the proposed AAQM method achieved the second-best delay of 28.51, while the BLUE method achieved the best delay of 13.18; however, the BLUE results are insufficient because of the low throughput. Consequently, the proposed AAQM method outperformed the compared methods with its superior throughput and acceptable delay.

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Application of active queue management for real-time adaptive video streaming

Cited by 18 publications

References 32 publications

Smart cities at risk: Systemic risk drivers in the blind spot of long‐term governance

Smart cities at risk: Systemic risk drivers in the blind spot of long‐term governance

Low latency 3D image streaming in medicine

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

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