A reliable overlay video transport protocol for multicast agents in wireless mesh networks

Sun

et al. 2015

Hypertext transfer protocol (HTTP) video streaming has occupied more than half of mobile Internet traffic today. Quality of experience (QoE) estimation for HTTP video streaming is significantly meaningful for network operators, as they can utilize valuable network resources more efficiently based on the QoE estimated. But, it is very hard to estimate QoE accurately with theoretical methods. In this paper, we adopt three buffering-related application metrics, which have been verified to be the main factors that influence the QoE of HTTP videos, together with three fundamental quality of service (QoS) metrics in network layer, to estimate QoE. We suggest a two-layered mapping model to capture the correlation between network QoS and application QoS and that between application QoS and QoE. Then, we exploit four regression methods, that is, linear regression, back propagation neural network, general regression neural network and support vector regression (SVR), to investigate the mapping relations of the network metrics, the application buffering-related metrics and QoE. Experimental results demonstrate that general regression neural network and SVR perform almost equally well in fitting ability, and SVR performs best among the four methods in terms of generalization ability. Finally, we analyze the influences of different network QoS parameters on QoE based on the experimental results.KEY WORDS: quality of experience (QoE); HTTP video streaming; mobile Internet; support vector regression (SVR); general regression neural network (GRNN); back propagation neural network (BPNN); quality of service (QoS); linear regression (LR)

“…• square sum of residuals S δ The definition of the square sum of residuals (S δ ) is shown in Equation (8).…”

Section: Performance Metrics For Regression Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quality of experience estimation with layered mapping for hypertext transfer protocol video streaming over wireless networks

Sun

et al. 2015

“…For such a scenario, it is usually required to construct a multicast tree to implement the proposed system, because multicast mechanism can deliver a message to a group of destinations in single transmission from the source. Furthermore, for supporting end users to receive high‐resolution TV programs, the multicast tree must have sufficient bandwidth to transmit high‐quality video streams on multiple links simultaneously . Hence, the capability of supporting multiple interference‐free concurrent transmissions is a requirement for our multicast tree.…”

Section: Introductionmentioning

confidence: 99%

A cross‐layer optimization for maximum‐revenue‐based multicast in multichannel multiradio wireless mesh networks

Yang

Hong

2013

SUMMARYGiven a video/audio streaming system installed on a multichannel multiradio wireless mesh network, we are interested in a problem concerning about how to construct a delay‐constrained multicast tree to support concurrent interference‐free transmissions so that the number of serviced mesh clients is maximized. In this paper, we propose a heuristic approach called cross‐layer and load‐oriented (CLLO) algorithm for the problem. On the basis of the cross‐layer design paradigm, our CLLO algorithm can consider application demands, multicast routing, and channel assignment jointly during the formation of a channel‐allocated multicast tree. The experimental results show that the proposed CLLO outperforms the layered approaches in terms of the number of serviced mesh clients and throughputs. This superiority is due to information from higher layers can be used to guide routing selection and channel allocation at the same time. As a result, the CLLO algorithm can explore more solution spaces than the traditional layered approaches. In addition to that, we also propose a channel adjusting procedure to enhance the quality of channel‐allocated multicast trees. According to our simulations, it is proved to be an effective method for improving the performance of the proposed CLLO algorithm. Copyright © 2013 John Wiley & Sons, Ltd.

“…In recent years, mobile and wireless applications are growing significantly, and it has been predicted that data traffic of wireless devices will exceed the traffic of wired devices by 2014 [1]. To facilitate future generations of wireless applications, a key technology is wireless mesh network (WMN) [2][3][4][5][6][7][8][9][10], which has been widely studied in the literature. In WMNs, the communication can be realized between wireless mesh clients beyond the transmission range of each other, because each wireless mesh routers in the WMN cooperates by forwarding data packets for other wireless mesh routers, which makes it highly flexible and robust.…”

Section: Introductionmentioning

confidence: 99%

An efficient communication relay placement algorithm for content‐centric wireless mesh networks

Zhang

et al. 2013

In this paper, we investigate a communication relay placement problem to optimize the network throughput in a content-centric wireless mesh networks (WMN), in which the WMN is enhanced by including a small set of communication relays and a subset of wireless mesh routers serving as storage nodes. Specifically, we first define the communication relay placement problem in content-centric WMNs. We then model the problem as a mathematical programming and propose a linear programming approach for calculating the achievable network throughput when the positions of communication relays are fixed. Next, to optimally placing the communication relays, we formulate an integer linear programming problem and we develop an efficient near-optimal approximation algorithm based on linear programming relaxation. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms. AN EFFICIENT RELAY PLACEMENT ALGORITHM FOR CONTENT-CENTRIC WMNS 263For the peer-to-peer traffic, it has been shown that the maximal throughput in large-scale two- 11,12], where W is the wireless channel capacity and n is the number of nodes in the network. Intuitively, such a capacity is because of the fact that the dominating traffic (in the scale of ‚.n/) has to be routed, via multihop paths, through a certain subset of mesh routers (in the scale of ‚. p n//. In other words, the subset of mesh routers is the bottleneck of the whole WMN.For the Internet access scenario, which is the most common one today, all traffic has to pass through a limited number of Internet gateways. Consequently, the network capacity is limited by the wireless channels of the gateways. For instance, if there is only one gateway, then the capacity of a large-scale two-dimensional WMN is ‚ W n [13]. To improve the performance of WMN, in this paper we consider a content-centric WMN design, in which the WMN is enhanced by including a small set of communication relays and a subset of wireless mesh routers serving as storage nodes.In the past, relays or local gateways have been discussed in many hybrid WMN architectures [14][15][16][17][18][19][20], as shown in Figure 1(a). The advantages of using relays are as the follows. Firstly, additional communication relays can improve the connectivity of the WMN, because they can interconnect clients in different areas and can also cover areas disconnected with each other. For example, in the traditional WMN, client m 1 cannot communicate with client m 3 because they are in different communication areas and the two areas are not connected. On the other hand, in the hybrid WMN shown in Figure 1 (b), client m 1 can communicate with client m 3 because their communication areas are connected by communication relays.Secondly, additional communication relays can decrease communication delay and increase network throughput. Communication relays are usually more powerful than regular node in that they can be equipped with multiple interfaces, directional antenna, improved transceivers...