On the impact of the video quality assessment in 802.11e ad-hoc networks using adaptive retransmissions

Abstract

This paper investigates the influence of the video quality assessment at the medium access control (MAC) layer of an 802.11e distributed network in which the purpose is to adapt the retry limit of each video packet for maximizing the quality perceived by the end user. Moving from an existing Markov model of the 802.11e access scheme, the number of retransmissions associated to each video packet is evaluated to satisfy the distortion requirements inferred using two quality assessment measures: the mean squared error (MSE) and the structural similarity (SSIM) index. The aim of the proposed work is to discuss the suitability of these measures for managing the retransmission policy in the presence of contention-based mechanisms. The numerical results are obtained using a network simulator implementing the 802.11e enhanced distributed channel access (EDCA) in which the video sequences are encoded using the H.264 scalable video coding (SVC) standard. © 2014 IEEE

Fast retry limit adaptation for video distortion/delay control in IEEE 802.11e distributed networks

“…Accordingly, several retry limit adaptation methods have been proposed in the research literature [5][6][7][8][9][10][11][12][13][14][15][16]. The QoS strategy presented in [5] adopts a priority queueing also at the network layer, in order to relate the adaptation to the tradeoff between drop probability and buffer overflow rate.…”

“…A fragment-based retransmission scheme suitable for video traffic is developed in [14], where the aim is to decrease the duration of the retransmission attempts in the presence of channel errors. In [16] the mean square error and the structural similarity are compared as video quality assessments for developing adaptive retransmission strategies. A tradeoff between energy efficiency and satisfaction of the QoS requirements for centralized operations is obtained in [15] by a joint dynamic adjustment of the contention window and of the retry limit.…”

“…In particular, the packets that lead to a higher distortion in the case of loss should be associated to a lower drop probability and hence to a higher retry limit, while the packets determining a lower distortion should be associated to a higher drop probability and hence to a lower retry limit. Moreover, the distortion should be not only inversely proportional to the drop probability, but should recall the exponential relationship in (20) between p drop and m 2,k , in order to provide an effective adaptation [16]. Therefore, imposing as a further requirement that the sum of the delays derived by (16)-(18) for the…”

“…The novel formulation in (24) for the time requirement in (22) allows the exploitation of the knowledge of the retry limits already evaluated for k < k. Now, using (16) and (17) in (24), one can solve the corresponding inequality in the integer retry limit that guarantees the satisfaction of the requirement on the expiration time, thus identifying the limiting value:…”

Estimation of the Bit Error Rate (BER) for Uplink Millimeter-Wave Line-of-Sight Communications

“…The mature implementation of the long-term evolution (LTE) system has boosted the interest of commercial operators towards the fifth-generation (5G) cellular network, whose placement on the market is expected to be realized by 2020 [1]. The forthcoming 5G technology foresees a pervasive wireless scenario capable to support the most evolved Internet of Things (IoT) applications, from high-definition video streaming to intelligent metering, from automated health to infrastructure management, home automation and public transportation [2][3][4]. Beside the provisioning of more capacity with respect to LTE, 5G is expected to be characterized by a major virtualization of the core and edge network, an ultra-densification of the deployed devices, and a shift of the frequency spectrum towards the millimeter-wave (mmWave) domain.…”