Abdel-Karim Al-Tamimi

A Scheduler for Unsolicited Grant Service (UGS) in IEEE 802.16e Mobile WiMAX Networks

Most of the IEEE 802.16e Mobile WiMAX scheduling proposals for real-time traffic using unsolicited grant service (UGS) focus on the throughput and the guaranteed latency. The delay variation or delay jitter and the effect of burst overhead have not yet been investigated. This paper introduces a new technique called swapping min-max (SWIM) for UGS scheduling that not only meets the delay constraint with optimal throughput, but also minimizes the delay jitter and burst overhead.

Dynamic resource allocation based on online traffic prediction for video streams

In this paper, we propose a new dynamic resource allocation (DRA) scheme to support the constantly increasing online video stream traffic, especially high definition (HD) video streams. Our DRA scheme is based on online traffic prediction using seasonal time analysis. Our scheme seeks to provide accurate traffic prediction, to minimize the resource negotiation frequency, and to increase the utilization of the network resources while meeting maximum delay requirements. We validate our approach using various video traces, including our video collection of more than 50 HD video traces. We show through our results that our proposed scheme achieve up to 19.8% improvement in allocating bandwidth for short-length video traces, and up to 25% for long traces compared to the variable step-size adaptive (VSA) algorithm.

QoE-Based Low-Delay Live Streaming Using Throughput Predictions

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.

ISAB: Integrated Indoor Navigation System for the Blind

Generally, indoor navigation is considered as a challenging task, especially when people navigate through an unfamiliar place (e.g. a university or a mall). It is even a more challenging endeavor for the visually impaired and blind community. This paper presents an innovative approach to the precise indoor navigation challenge for the blind individuals using a multi-tier solution with the help of an intuitive smartphone interface. We utilize a set of different communication technologies (WiFi, Bluetooth and radio-frequency identification) to help users reach an object with high accuracy. As a proof of concept, we deploy a fully functional testbed and we evaluate our entire solution inside our university library by helping blind users find a specific book. Our results demonstrate the high accuracy of the proposed system to reach an object with accuracy up to 10 cm. The intuitive smartphone interface provides step-by-step navigation voice instructions of the least hazardous path for the blind users while minimizing the cognitive load on their short-term memory. In addition, we show that our iterative improvements on our smartphone’s interface has improved the system’s efficiency and its accuracy in reaching specific objects successfully.

A power efficient 3-Gbits/s 1.8V PMOS-based LVDS output driver

This paper presents a new topology of a PMOS based LVDS voltage-mode output driver. This topology is designed to meet the requirements of low power consumption and high data rates applications. The driver, which consists of a pre-driver stage and an output stage, uses a positive feedback technique at the output stage to achieve line impedance matching and power saving. The pre-driver stage is used to set the drivers swing and common mode output voltage. The pre-driver and the output stage consume only 9mW of power at 3 Gbps speed while operating from a 1.8V voltage supply. The system is designed and simulated using CMOS 180nm technology and is fully compliant with LVDS output swing and common mode voltage specifications.

Parallel scheme for real-time detection of photosensitive seizures

The production and distribution of videos and animations on gaming and self-authoring websites are booming. However, given this rise in self-authoring, there is increased concern for the health and safety of people who suffer from a neurological disorder called photosensitivity or photosensitive epilepsy. These people can suffer seizures from viewing video with hazardous content. This paper presents a spatiotemporal pattern detection algorithm that can detect hazardous content in streaming video in real time. A tool is developed for producing test videos with hazardous content, and then those test videos are used to evaluate the proposed algorithm, as well as an existing post-processing tool that is currently being used for detecting such patterns. To perform the detection in real time, the proposed algorithm was implemented on a dual core processor, using a pipelined/parallel software architecture. Results indicate that the proposed method provides better detection performance, allowing for the masking of seizure inducing patterns in real time.

Modeling VP8/WebM Encoded Video Traces

In this paper, we present our research results in modeling video traces encoded with VP8/WebM codec. Our research results are based on our collection of more than 800 VP8 encoded video traces. We show in this paper that our simplified seasonal ARIMA (SAM) model provides a valid model for WebM encoded video traces regardless of their motion, texture levels, or encoding settings. Additionally, we compare the goodness-of-fit of SAM model against simple autoregressive (AR) and automatic ARIMA modeling methods using both visual and statistical tests. Our results show the validity of SAM model as a VP8 video traces model, and its superiority to the other compared models. We conclude this paper with a discussion of the implications of our findings on related areas of research. Keywords— Modeling, Workload Modeling and Characterization, Video Traces, WebM, VP8, Seasonal ARIMA, SAM model, YouTube.

Modeling and dynamic resource allocation for high definition and mobile video streams

Video streaming traffic has been surging in the last few years, which has resulted in an increase of its Internet traffic share on a daily basis. The importance of video streaming management has been emphasized with the advent of High Definition (HD) video streaming, as it requires by its nature more network resources. In this dissertation, we provide a better support for managing HD video traffic over both wireless and wired networks through several contributions. We present a simple, general and accurate video source model: Simplified Seasonal ARIMA Model (SAM). SAM is capable of capturing the statistical characteristics of video traces with less than 5% difference from their calculated optimal models. SAM is shown to be capable of modeling video traces encoded with MPEG-4 Part2, MPEG-4 Part10, and Scalable Video Codec (SVC) standards, using various encoding settings. We also provide a large and publicly-available collection of HD video traces along with their analyses results. These analyses include a full statistical analysis of HD videos, in addition to modeling, factor and cluster analyses. These results show that by using SAM, we can achieve up to 50% improvement in video traffic prediction accuracy. In addition, we developed several video tools, including an HD video traffic generator based on our model. Finally, to improve HD video streaming resource management, we present a SAM-based delay-guaranteed dynamic resource allocation (DRA) scheme that can provide up to 32.4% improvement in bandwidth utilization.