## 1 Introduction
If we allocate the required QoS statically as per the demand of the application, then we get the guarantee for the desired QoE. However, the allocated network resources are unexploited most of the time. Figure 1: A high-level basic architecture design of the teleoperation system. To this end, there is a strong need for a strategy to achieve the tradeoff between stability and transparency of the system and meet the QoS and QoE requirements under variable time delay and high packet rate. Therefore, in this paper, an RL-based dynamic switching strategy has been proposed where the haptic codecs govern the allocated environment's (operator/teleoperator) dynamics and network resources. The proposed strategy estimates the network resources in real-time and utilizes one of the famous RL algorithms, Q-learning, to select the optimal haptic codec schemes. The proposed switching strategy aims to gain both stability and transparency while meeting the stringent QoS and QoE requirements of tactile Internet services like teleoperations. The proposed strategy collects the Explicit Congestion Notification (ECN) marking packets during communication and inputs to the Long Short-Term Memory (LSTM) model to estimate the congestion levels in the network and select the best codec scheme to reduce the congestion and provide the desired QoS and QoE. The core network design of the proposed system comprises SDN, and MEC technology to realize reduce network latency and support Tactile Internet applications. As we discussed, the SDN paradigm reduces the network complexity and provides dynamic sharing of network resources. Moreover, it provides flexibility to the network, where the operators can define virtual slices corresponding to use cases with a focus to ensure QoS. The major contributions of this paper are summarized below as:  
* We discuss the Tactile Internet challenges in the context of high pack rate/packet size and extensively review different haptic codec solutions and summarized their key contributions. * An Intelligent Tactile Edge (ITE) framework is proposed that incorporates the ECN mechanism, Machine Learning (ML), and particularly, RL policies for optimal haptic codecs selection based on network congestion level employing the Markov decision process (MDP). * In the proposed framework, an LSTM model is utilized to predict the congestion level in the network by exploiting ECN information in the TCP header of the data packet. To select the best haptic codec scheme corresponding to the network congestion, an RL-based Q-learning algorithm is employed. Figure 2: Dynamic interaction of the operator (master) with remote virtual application (slave) through a Falcon haptic device, (**a**) represents the positions of the human operator’s hand at master side, (**b**,**c**) illustrates the in-depth exploration of position at x-axis. * We conduct different experiments to demonstrate that the proposed system selects the best haptic data compression and reduction scheme for haptic-enabled bilateral teleoperations to ensure QoS and QoE requirements. The remainder of this paper is structured as follows. Section 2 provides state-of-the-art research work on haptic codec schemes to provision QoS/QoE for haptic-driven teleoperation services. Section 3 presents the proposed strategy to predict network congestion and select optimal solutions to the corresponding QoS and QoE parameters. Section 4 demonstrates the experimental results in terms of QoS and QoE provisioning. Section 5 presents a discussion and future challenges. Finally, Section 6 concludes this paper and presets possible future directions.