An End-to-End Content-Aware Congestion Control Approach for MPEG Video Transmission

2008 ◽  
Author(s):  
Hongyun Yang ◽  
Xuhui Chen ◽  
Ruiming Hu
Keyword(s):  
Congestion Control ◽  
Video Transmission ◽  
Control Approach ◽  
End To End ◽  
Content Aware

ICMA: AN EFFICIENT INTEGRATED CONGESTION CONTROL APPROACH

2019 ◽  
Vol 14 ◽  
Author(s):  
Tayyab Khan ◽  
Karan Singh ◽  
Kamlesh C. Purohit
Keyword(s):  
Congestion Control ◽  
Packet Loss ◽  
Control Method ◽  
Multicast Communication ◽  
Bandwidth Utilization ◽  
File Transfer ◽  
Control Approach ◽  
Communication Technique ◽  
Control Scheme ◽  
Multiparty Video Conferencing

Background: With the growing popularity of various group communication applications such as file transfer, multimedia events, distance learning, email distribution, multiparty video conferencing and teleconferencing, multicasting seems to be a useful tool for efficient multipoint data distribution. An efficient communication technique depends on the various parameters like processing speed, buffer storage, and amount of data flow between the nodes. If data exceeds beyond the capacity of a link or node, then it introduces congestion in the network. A series of multicast congestion control algorithms have been developed, but due to the heterogeneous network environment, these approaches do not respond nor reduce congestion quickly whenever network behavior changes. Objective: Multicasting is a robust and efficient one-to-many (1: M) group transmission (communication) technique to reduced communication cost, bandwidth consumption, processing time and delays with similar reliability (dependability) as of regular unicast. This patent presents a novel and comprehensive congestion control method known as integrated multicast congestion control approach (ICMA) to reduce packet loss. Methods: The proposed mechanism is based on leave-join and flow control mechanism along with proportional integrated and derivate (PID) controller to reduce packet loss, depending on the congestion status. In the proposed approach, Proportional integrated and derivate controller computes expected incoming rate at each router and feedback this rate to upstream routers of the multicast network to stabilize their local buffer occupancy. Results: Simulation results on NS-2 exhibit the immense performance of the proposed approach in terms of delay, throughput, bandwidth utilization, and packet loss than other existing methods. Conclusion: The proposed congestion control scheme provides better bandwidth utilization and throughput than other existing approaches. Moreover, we have discussed existing congestion control schemes with their research gaps. In the future, we are planning to explore the fairness and quality of service issue in multicast communication.

scholarly journals Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 607 ◽  
Author(s):  
Ihab Ahmed Najm ◽  
Alaa Khalaf Hamoud ◽  
Jaime Lloret ◽  
Ignacio Bosch
Keyword(s):  
Machine Learning ◽  
Congestion Control ◽  
Learning Model ◽  
Training Dataset ◽  
Transmission Protocol ◽  
Control Mechanisms ◽  
Next Generation ◽  
Control Approach ◽  
Machine Learning Model ◽  
Prediction Approach

The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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