Recently, Hypertext Transfer Protocol (HTTP)-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to the varying network conditions to ensure a high quality of experience (QoE)—that is, minimize playback interruptions while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless access network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 20 to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (short for low-latency prediction-based adaptation), which is designed to operate with a transport latency of a few seconds. To reach this goal, LOLYPOP leverages Transmission Control Protocol throughput predictions on multiple time scales, from 1 to 10 seconds, along with estimations of the relative prediction error distributions. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the QoE by maximizing the average video quality as a function of the number of skipped segments and quality transitions. To select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions, limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm called FESTIVE . We observed that the average selected video representation index is by up to a factor of 3 higher than with the baseline approach. We also observed that LOLYPOP is able to reach points from a broader region in the QoE space, and thus it is better adjustable to the user profile or service provider requirements.

WiMAX has attracted a lot of attention recently in the telecommunication community including researchers, product developers, and service providers. Numerous papers have been published on various design issues of WiMAX networks. Since the issues being discussed are usually ahead of product availability, most of these studies require simulation. It is important to have some common features among these models so that their results can be compared. Therefore, the Application Working Group of the WiMAX Forum has developed a standard simulation methodology that describes the key features to be simulated, the method of simulating these features, and various parameter values to be used. This system-level methodology has been used in several public and commercial WiMAX simulation models. This article presents a summary of the simulation methodology, and discusses the key issues and common mistakes in simulating various features of network configuration, and the physical, MAC, and application layers.

The key to proper resource allocation for mobile video on wireless networks is to have a good model for the resource demands. In this paper, we present the results of analysis of a number of mobile video streams and show that a simple seasonal ARIMA model (SAM) can provide a very good representation for both MPEG4-Part2 and MPEG4-Part10 videos, the formats that are commonly used for mobile videos. The model has been implemented to provide both video frame and RTP packet generators. The model can be used to represent different movies and can be easily adjusted to produce different workloads for simulation studies. We use the SAM generator to compare the performance of three different scheduling methods for video over WiMAX networks: Earliest Deadline First (EDF), Deficit Round Robin (DRR) and a combination of the two. The results show that under overload, EDF introduces unfairness. DRR with deadline is fair and gives the best performance.

The Perceptual Evaluation of Audio Quality (PEAQ) is a standard ITU algorithm that is widely used in audio quality evaluation. This algorithm is reported to be accurate for high quality audio signal with low impairments that are caused by lossy compression associated with audio codecs. In this paper, we experimentally and quantitatively show that PEAQ fails drastically under very small imperceptible impairments that are caused by frames' losses associated with audio streaming applications over lossy networks or due to audio recording and processing errors, which make it not preferable evaluation tool for these applications.