ESM: energy-efficient bitrate adaptation for video streaming with deep reinforcement learning

QoE-driven adaptive bitrate selection for cloud–edge 5G video streaming using Bi-LSTM and XGBoost

The video over 5G networks is accelerating its rapid growth. Adaptive bit-rate selection mechanism intelligence is introduced to achieve the optimal user Quality of Experience (QoE) in dynamic conditions of the network. This paper presents a quick and intelligent QoE-based adaptive bit rate selection system for 5G vide...

An efficient on-policy actor–critic architecture for adaptive bitrate streaming using deep reinforcement learning

The proposed GRASP algorithm outperforms Comyco and other cutting-edge DRL-based ABR methods under a variety of network situations and reduces the stall ratio compared to Comyco up to 67% and 70% for 5G and 4G networks respectively.

DIA: A Deep Reinforcement Learning-Based Interest-Aware ABR Scheme for Cloud Gaming

A Deep Reinforcement Learning-based Interest-Aware Adaptive Bitrate (ABR) scheme, called DIA, that guarantees the quality of perceptually key objects in each frame, while dynamically allocating lower resolutions for non-key objects to conserve bandwidth under constrained network conditions is proposed.

MamBRA: session-level bandwidth prediction for adaptive video streaming using selective state space models

TwinVFI-PPO: Uncertainty-Aware Joint Bitrate Adaptation and Frame Interpolation for Neural Video Streaming

Adaptive bitrate (ABR) control and client-side video frame interpolation (VFI) are usually optimized as separate modules, even though they compete for the same playback, bandwidth, and compute budgets and jointly determine rendered quality. We present TwinVFI-PPO, an uncertainty-aware framework that selects bitrate and...

Quality-oriented hot data management using deep reinforcement learning in video storage systems