# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 1 Introduction
These compression standards adopt a hybrid coding framework composed of prediction coding, transform coding, quantization, filter processing, and entropy coding modules. With the development of technology, there exist many extended processing methods within each coding module. For example, predictive coding has forward prediction, backward prediction, and bidirectional prediction. Although these optimized encoding methods improve encoding efficiency, they come at a cost of increased computational complexity [17; 18; 19]. 3. Transmission  
Network transmission is subjected to real-time traffic conditions. Thus, it is necessary to set buffers in the coding end, decoding end, and within the network routers to ensure limited data loss. Network congestion has a significant impact on the network. In the event of a bufferbloat [20], it will lead to long queue delays [21; 22]. The fluctuating video bit rate will also cause network congestion, resulting in transmission delay and buffering delay of subsequent video streams [23]. To alleviate these issues, congestion controls in transmission protocols [24] are developed. The introduction of these buffers will directly increase the overall latency of the transmission system. 4. Display  
The latency of most current monitors can be found on the corresponding website [25]. Due to the influence of E-sports games, monitors with a latency of only a few milliseconds have been introduced into the market. In order to meet the latency requirements in different application scenarios, it is necessary to accurately perceive the end-to-end latency of video transmission. At present, there exist limited measurement solutions for video transmission latency. A common method is to use a simple side-by-side shooting method. A time counter is placed next to the transmission result display screen, the value of the time counter is displayed in the screen content, and the difference between the two times is used as the latency measurement value. This method is simple and easy to operate. The measurement can be completed with two mobile phones combined with the video transmission system, but the accuracy is low, not continuous, and cost prohibitive for permanent operation. The work in [26] is specifically designed to measure the end-to-end delay of computer video chat applications. The sending computer displays a barcode representing the time on the screen, and the transmitting receiver reads the barcode on the video chat screen to obtain the sending time. The accuracy of the method cannot be guaranteed due to the influence of screen refresh and software execution speed. Reference [27] is used to measure the end-to-end delay of a video surveillance system on the same computer. There is a bar screen on which the computer displays changes at fixed times. The digital surveillance camera captures the change video and transmits it to the computer for decoding and compares it with the change sending time to obtain the end-to-end delay. This method has limited measurement accuracy and application scenarios. Reference [28] mainly focuses on the evaluation of QUIC on Web, cloud storage, and video workloads, focusing on the generation and measurement of network transmission delay. This measurement does not include the delay caused by a codec, buffer, etc. Reference [29] introduces the delay model of end-to-end transmission of real-time video, which is composed of sub-models of capture, encoding, network, decoding, rendering, and display refresh. However, this method is only used in simulation experiments without actual measurement. In reference [30], in order to reduce the delay of glass to glass (G2G) and the glass to glass algorithm (G2A), frame hopping and preemption methods are proposed to reduce the delay, and the delay measurement model is built by using light-emitting diodes (LED) and phototransistors (PT). However, the measurement accuracy can only reach 0.5 ms.