Statistical Multiplexing: Flexible Allocation of Communication Resources

# 1. Overview of Statistical Multiplexing Technology

## 1.1 Differences between Traditional Multiplexing and Statistical Multiplexing

Traditional multiplexing techn***mon techniques include Time Division Multiplexing (TDM), Frequency Division Multiplexing (FDM), and Code Division Multiplexing (CDM). These techniques achieve resource sharing among multiple users by dividing time, frequency, or code sequences, but they often suffer from low resource utilization and difficulties in adapting to dynamic demands.

In contrast, statistical multiplexing employs a dynamic approach to allocate resources based on actual demand on communication channels. It collects statistical characteristics of data streams from different users and dynamically schedules them according to their needs, leading to more flexible and efficient resource allocation.

## 1.2 Current Application of Statistical Multiplexing in Communications

Statistical multiplexing is widely applied in modern communication systems. In the Internet, traffic control and congestion control mechanisms of the TCP/IP protocol are examples of statistical multiplexing. They dynamically adjust data transmission rates based on network congestion levels, ensuring stable network communications.

Video streaming, audio transmissions, and other applications also extensively use statistical multiplexing. For instance, in video streaming, more bandwidth can be dynamically allocated to parts of the video based on user interest, improving video playback smoothness and clarity.

In summary, statistical multiplexing plays a crucial role in enhancing the utilization of communication resources and meeting diverse communication service needs, holding significant importance for the future development of communication networks.

# 2. How Statistical Multiplexing Works

Statistical multiplexing is a key technique in communication networks for the flexible allocation of communication resources. It dynamically allocates data packets based on the needs of various communication services to improve bandwidth utilization and reduce waste. This chapter will delve into the detailed mechanism of statistical multiplexing.

#### 2.1 Dynamic Allocation of Data Packets

The core of statistical multiplexing lies in the dynamic allocation of data packets. In traditional multiplexing, communication resources are statically assigned to various communication services, leading to inefficient utilization and resource wastage. Statistical multiplexing, however, allocates resources on demand, dynamically scheduling them according to real-time needs to maximize the use of bandwidth resources in the communication network.

The dynamic allocation process is achieved through a scheduling algorithm that determines resource allocation based on various metrics such as packet size, priority, and arrival time. Resource scheduling algorithms based on demand can flexibly allocate resources to meet the needs of different communication services.

#### 2.2 Demand-Based Resource Scheduling Algorithms

Demand-based resource scheduling algorithms are crucial for the implementation of dynamic allocation in statistical multiplexing. These algorithms prioritize packets based on communication service needs, ***mon demand-based resource scheduling algorithms include Minimum Transmission Time First (MTTF) and Minimum Delay Variation First (MDVF).

In the MTTF algorithm, packet transmission time is a significant metric. It prioritizes scheduling shorter transmission time packets to complete transmissions faster. In the MDVF algorithm, packet jitter is a key metric, and the algorithm prioritizes packets with less jitter to ensure stability and delay control in communication services.

#### 2.3 Real-Time Traffic Control and Scheduling Strategies

Real-time traffic control is a significant issue in statistical multiplexing. Real-time traffic has strict temporal requirements and higher priority. If not transmitted promptly, it can lead to severe quality issues. To address the control of real-time traffic, appropriate scheduling strategies