How can network latency be optimized for real-time applications?
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Network latency can be optimized for real-time applications through various methods. One approach is to select edge computing systems located nearby base stations to provision latency critical applications and transfer transmission specific data in real time . Another method involves using QUIC, a UDP-based protocol, and multipath transmission to transmit data simultaneously over multiple network paths and interfaces . Additionally, a reference point value function can be used to determine the latency requirements of the application, and a time variable point value function can calculate the actual point value based on current latency and throughput requirements . Furthermore, the development of next-generation Wi-Fi technology, such as IEEE 802.11be, includes mechanisms like EDCA operation to allocate priority order traffic for real-time applications, reducing average latency . Finally, optimizing networks with irregular topologies using genetic algorithms can help achieve optimized latency and support real-time tasks .
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| Papers (5) | Insight |
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23 Aug 2021 7 Citations | The paper proposes a new EDCA operation for supporting real-time applications in Wi-Fi networks, which reduces average latency by 76.57% compared to the original mechanism. |
| The paper proposes optimizing network latency for real-time applications by using a genetic algorithm to design networks with irregular topologies. | |
| The paper discusses a method for optimizing network latency for real-time applications by continuously adapting the applications to current transmission conditions in the network. | |
12 Oct 2020 8 Citations | The paper discusses the use of QUIC, a UDP-based protocol, and multipath transmission to address the challenges of real-time applications. It presents a scheduler called NineTails that reduces outlier latencies and improves Quality of Experience (QoE) in video streaming. |
| The paper discusses a method for optimizing network latency for real-time applications by selecting an edge computing system and continuously optimizing resource allocation based on the current conditions in the cell. |
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