The use of network delay estimation for multimedia data retrieval

Abstract

Multimedia data have specific temporal presentation requirements. For example in video conferencing applications voice and images of participants must be delivered and presented synchronously. These requirements can be achieved by scheduling or managing system resources. We present a technique called limited a priori scheduling (LAP) to manage the delivery channel from source to destination for digital multimedia data. By using delay estimation a LAP scheduler can retrieve stored digital media spanning arbitrary networks with unspecified delays. The use of delay estimation also facilitates selective degradation of service in bandwidth and buffer limited situations. Such degradation enables the continuous real-time playout and synchronization of various media arriving from different sources. The performance of the LAP scheduler is described based on implementation and experimentation using Ethernet.

A hierarchical control protocol for group-oriented playbacks supported by content distribution networks

“…The distribution of the normal variable is truncated at ÀK f d m in order to assure that d i cannot assume negative values. Normal distribution is chosen according to the considerations presented in Gibbon and Little (1996). The parameters of the delay model are set (Fortino et al, 2007).…”

Adaptive playout scheduling and loss concealment for voice communication over ip networks

“…A good estimation of the network delay is therefore an important component for adaptive playout scheduling. Known techniques for delay estimation include linear recursive filtering with stochastic gradient algorithms [1], histogram based approaches [2] [7], normal approximation [14], and event-counting [6]. Here we use the delay of past packets and base our estimation on its order statistics in order to adapt the playout schedule to the network variations in a more reactive way.…”

Cooperative control of multicast-based streaming on-demand systems

“…The distribution of the normal variable is truncated to −K f δ m in order to assure that δ i cannot assume negative values. The choice of the normal distribution was made according to the considerations presented in [14]. The delay model parameters were set as follows: δ m = 0.1 s, which accommodates small regional areas, and K f = 0.7, which limits the delay variability.…”