Abstract-Interest in broadband wireless access (BWA) has been growing due to increased user mobility and the need for data access at all times. IEEE 802.16e based WiMAX networks promise the best available quality of experience for mobile data service users. Unlike wireless LANs, WiMAX networks incorporate several quality of service (QoS) mechanisms at the Media Access Control (MAC) level for guaranteed services for data, voice and video. The problem of assuring QoS is basically that of how to allocate available resources among users in order to meet the QoS criteria such as delay, delay jitter and throughput requirements. IEEE standard does not include a standard scheduling mechanism and leaves it for implementer differentiation. Scheduling is, therefore, of special interest to all WiMAX equipment makers and service providers. This paper discusses the key issues and design factors to be considered for scheduler designers. In addition, we present an extensive survey of recent scheduling research. We classify the proposed mechanisms based on the use of channel conditions. The goals of scheduling are to achieve the optimal usage of resources, to assure the QoS guarantees, to maximize goodput and to minimize power consumption while ensuring feasible algorithm complexity and system scalability.

We present a simple analytical method for capacity evaluation of IEEE 802.16e Mobile WiMAX networks. Various overheads that impact the capacity are explained and methods to reduce these overheads are also presented. The advantage of a simple model is that the effect of each decision and sensitivity to various parameters can be seen easily. We illustrate the model by estimating the capacity for three sample applications—Mobile TV, VoIP, and data. The analysis process helps explain various features of IEEE 802.16e Mobile WiMAX. It is shown that proper use of overhead reducing mechanisms and proper scheduling can make an order of magnitude difference in performance. This capacity evaluation method can also be used for validation of simulation models.

Abstract-Mobile WiMAX systems based on the IEEE 802.16e standard require all downlink allocations to be mapped to a rectangular region in the two dimensional subcarrier-time map. Many published resource allocation schemes ignore this requirement. It is possible that the allocations when mapped to rectangular regions may exceed the capacity of the downlink frame, and the QoS of some flows may be violated. The rectangle mapping problem is a variation of the bin or strip packing problem, which is known to be NP-complete. In a previous paper, an algorithm called OCSA (One Column Striping with nonincreasing Area first mapping) for rectangular mapping was introduced. In this paper, we propose an enhanced version of the algorithm. Similar to OCSA, the enhanced algorithm is also simple and fast to implement; however, eOCSA considers the allocation of an additional resource to ensure the QoS. eOCSA also avoids an enumeration process and so lowers the complexity to O(n 2 ).

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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.

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