Optimal voice packet classification for enhanced VoIP over priority-enabled networks

This paper proposes a method for optimal classification of voice packets to enhance the quality of voice communications over priority-enabled networks when poor transmission conditions occur. Either high or low priority is assigned to each packet according to the relevance of its payload (voice segment) for the voice intelligibility. Then, in case of constrained networking conditions, by discarding first the voice packets of lower importance, the network always delivers those segments that most contribute to the perceptual quality. The proposed method is based on a dynamic programming optimisation algorithm that finds the optimal subset of m high priority voice segments in each utterance of size n > m. Such optimal subset minimizes the reconstruction distortion over all possible subsets with the same size m (i.e., the distortion incurred by a utterance reconstructed from only m segments). The simulation results show that the proposed method consistently achieves higher mean opinion scores (MOS) in comparison with non-selective packet drop under the same random network loss conditions, yielding better quality of experience (QoE) for the same packet loss rates (PLR). The priority classification algorithm is independent from error concealment methods and distortion metrics used in the optimisation process, which allows generalisation for diverse communication networks and applications.