<title>Framework model for packet loss metrics based on loss runlengths</title>

Abstract

For the same long-term loss ratio, different loss patterns lead to different application-level Quality of Service (QoS) perceived by the users (short-term QoS). While basic packet loss measures like the mean loss rate are widely used in the literature, much less work has been devoted to capturing a more detailed characterization of the loss process. In this paper, we provide means for a comprehensive characterization of loss processes by employing a model that captures loss burstiness and distances between loss bursts. Model parameters can be approximated based on run-lengths of received/lost packets. We show how the model serves as a framework in which packet loss metrics existing in the literature can be described as model parameters and thus integrated into the loss process characterization. Variations of the model with different complexity are introduced, including the well-known Gilbert model as a special case. Finally we show how our loss characterization can be used by applying it to actual Internet loss traces.

A Model for Correlated Rician Fading Channels Based on a Finite Queue

“…Sanneck and Carle [54] used an M th-order Markov chain process to describe the dependencies between packet losses. However, their models have a complexity (number of parameters) that exponentially grows with M , rendering it impractical for the modeling of packet loss processes with large memory.…”

Signature Amortization Using Multiple Connected Chains

“…The reason to choose these packets as non-contiguous is that Internet packet loss is burst in nature, and if a packet P i is lost, packet P i+1 is likely to be lost [2], [18], [19]. We mean by non-contiguous packets that the next packet to P i is P i+j where j > i + 1.…”

Real-time error protection of embedded codes for packet erasure and fading channels

“…The experiment shows that a mismatch between the optimization settings and the actual statistics can lead to a significant drop in quality. However, when a feedback channel is available, which is the typical situation in practice, the channel can be monitored (see [15] and [16] for channel estimation), allowing the transmitter to optimize the cost functions online with up-to-date channel statistics. The optimization of the cost functions also requires the source statistics, which may not be available offline.…”