Resource allocation in visual sensor networks using a reinforcement learning framework

Abstract

In recent years, video delivery over wireless visual sensor networks (VSNs) has g ained increasin g attention. The lossy compression and channel errors that occur durin g wire less multimedia transmissions can de g rade the quality of the transmitted video sequences. This paper addresses the problem of cross-layer resource allocation amon g the nodes of a wireless direct-sequence code division multiple access (DS-CDMA) VSN.The optimal g roup of pictures (GoP) len g th durin g the encodin g process is also considered, based on the motion level of each video sequence. Three optimization criteria that optimize a different objective function of the video qualities of the nodes are used. The nodes' transmission parameters, i.e., the source codin g rates, channel codin g rates and power levels can only take discrete values. In order to tackle the resultin g optimization problem, a reinforcement learning (RL) strate g y that promises efficient exploration and exploitation of the parameters' space is employed. This makes the proposed methodolo g y usable in lar g e or continuous state spaces as well as in an online mode.Experimental results hi g hli g ht the efficiency of the proposed method.

Resource allocation and visual quality estimation for wireless video transmission

“…Last but not least, the specific RL approach gives the opportunity to the proposed scheme to be used in an online mode. Hence, the specific research effort has resulted to a conference publication presented in [141].…”