Traffic Optimization and Multi-sided Pricing in Congested Networks

2018 ◽  
pp. 372-386
Author(s):  
Haroun H. Salih ◽  
Dina A. Urusova ◽  
Sergey A. Vasilyev
Keyword(s):  
Congested Networks ◽  
Traffic Optimization

Optimal Time-Varying Flows on Congested Networks

1987 ◽  
Vol 35 (1) ◽  
pp. 58-69 ◽  
Author(s):  
Malachy Carey
Keyword(s):  
Optimal Time ◽  
Time Varying ◽  
Congested Networks

scholarly journals Improving Perceived Quality of Live Adaptative Video Streaming

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 948
Author(s):  
Carlos Eduardo Maffini Santos ◽  
Carlos Alexandre Gouvea da Silva ◽  
Carlos Marcelo Pedroso
Keyword(s):  
Video Streaming ◽  
Short Term Memory ◽  
Transmission Rate ◽  
Video Quality ◽  
Video On Demand ◽  
Live Streaming ◽  
Network Congestion ◽  
Congested Networks ◽  
Over The Top

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

Agent-based modelling of car platooning for traffic optimization

2021 ◽  
Author(s):  
Michal Lemiec ◽  
Karol Malinowski ◽  
Mateusz Szymonski ◽  
Maria Ganzha ◽  
Marcin Paprzycki
Keyword(s):  
Agent Based ◽  
Agent Based Modelling ◽  
Traffic Optimization
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