## 2 Related Research Work  
In the literature, various schemes have been suggested to address the communication challenges, such as the high packet rate of the haptic data and the time-varying delay of the network. The haptic data sampling rate challenge imposes a strong demand to packetize and transmit haptic information with a data rate 1 kHz or higher to provide stability and transparency to the teleoperation systems [20]. In [21], an approach termed as Opportunistic Adaptive Haptic Sampling (OAHS) was proposed to provide a higher data rate and signal transmission during haptic communication over the network. The Webers fraction was employed to guarantee the QoS requirement of Tactile-driven services. A data-driven technique to decrease the packet rate of haptic data in case of limited channel bandwidth was proposed in [22] with a focus to guarantee QoS and QoE of the communication system. The authors claim that their proposed scheme improves the application service quality while minimizing computational complexity and latency. The effectiveness of the data-driven was evaluated based on energy consumption. The recent work in [23] presents a Perception-Lossless Codec (PLC) approach to meet the E2E delay requirement of the haptic-derive applications of the Tactile Internet. It utilizes run-length coding to reduce the coding delay, and coding loss and improves the Rate-Distortion (RD) performance. The work in [24] offers a Perceptual Vibrotactile Coding (PVC) derived from Sparse Linear Prediction (SLP) combining a cutaneous sensitivity model to assist haptic-driven applications. PVC-SLP scheme calibrates the filter parameter from the vibrotactile signal to produce signals. The performance of the PVC-SLP scheme was measured on two public datasets, with 280 and 1001 vibrotactile signals. It is considered a reference standard for tactile codecs. In [25] a Perceptual Wavelet Quantization (VC-PWQ) was proposed for vibrotactile codecs, where input signals are split into multiple blocks to feed the Wavelet which then inputs these blocks into a different frequency spectrum. Then perceptual thresholds were measured using a psychophysical model to lower the signal correlations. The presented method performed well in terms of SNR and PSNR across standard datasets with 280 vibrotactile samples using various compression ratios The authors in [26] explored the bursty haptic traffic problems of high packet rate during teleoperation that exposes several network issues like transmission delay and packet failure. A peak-suppressing adaptive Perceptual Deadband (PDb) approach was proposed which dynamically manages the packet rate based on earlier packet transmission traces. The proposed algorithm is capable of adjusting the PDb with a focus to minimize transmission delay. Similarly, the study in [27] presents a perceptual-based adaptive sampling scheme to automatically predict the just noticeable difference and reduce the traffic rate in real-time. The proposed approach adjusts the Weber threshold without prior knowledge to maintain the QoE. However, their proposed solution only outperformed when compared against a predetermined set of QoS and QoE measures, such as a system with no communication delays that is also transparent. To maximize the QoE and improve the RD, a deep neural network (DNN)-based end-to-end vibrotactile autoencoder was presented in [28]. Table 1 provides a summary of recently proposed haptic data codecs to enhance QoS and QoE provisioning for teleoperation systems. The improved QoS/QoE factors column indicates which QoS and QoE metrics were improved by the reviewed study. This includes the different haptic quality measuring metrics such as Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Haptic Structural Similarity Index Measure (HSSIM), Mean Square Error (MSE), and Mean Opinion Score (MOS). The MOS is a subjective QoE evaluation metric while others are objective metrics.