Reinforcement Learning-Based Power-Saving Algorithm for Video Traffics Considering Network Delay Jitter

Abstract

Recent studies on energy efficiency and scheduling of power-saving mode have been considered as key technologies for reducing the energy consumption of device-to-device (D2D) communication. Wi-Fi Direct (P2P), one of the key protocols for D2D communication, defines the on-off power saving mechanic called the notice of absence (NoA) power-saving mode that can be applied to the multimedia video traffic. The on-off power saving mechanic enables the user to transmit or receive the real-time video frame during the awake interval in which the video frame rate should meet the requirement. When the user can wholly transmit one video frame before the end time of a required inter-frame interval, it can switch to the sleep mode to save the power consumption. However, the challenge remaining for the NoA method is the fixed length of awake/sleep interval, even if the traffic load is varied. Therefore, in this study, we proposed a reinforcement learning-based power saving (RLPS) method to enhance the performance of the notice of absence (NoA) power-saving mode in Wi-Fi direct with taking the multimedia video transmission and the network delay jitter into consideration. The proposed RLPS method enables the Wi-Fi direct device to dynamically estimate the length of awake interval for transmitting the future video frame in real-time. In addition, the Wi-Fi direct device may wake up too early before the arrival of the video frame, which is caused by the network delay jitter. Thus, the client device has to wait for receiving the video frame. To tackle this challenge, the proposed RLPS method enables the device to predict the start time of awake interval for the purpose of reducing the delay time for receiving the upcoming video frame. Results show that the proposed RLPS method outperforms the existing NoA power-saving mode in terms of the outage probability, energy consumption, and transmission delay of Wi-Fi Direct devices.

Keywords

• Wi-Fi direct
• opportunistic power saving
• NoA
• reinforcement learning
• network delay jitter