Resource reservation for mobile hotspots in vehicular environments with cellular/WLAN interworking
- Wei Song^{1}Email author
https://doi.org/10.1186/1687-1499-2012-18
© Song; licensee Springer. 2012
Received: 30 June 2011
Accepted: 17 January 2012
Published: 17 January 2012
Abstract
Nowadays, the pervasive wireless networks enable ubiquitous high-rate wireless access from everywhere. There have been extensive studies on interworking of complementary wireless technologies in an indoor (residential or business) environment such as offices, hotels, and airport terminals. Nonetheless, there are ever-increasing demands for systematic deployment of moving networks in a vehicular environment such as public transits (e.g., a bus, train, or airplane). Due to the high mobility, it is very challenging to deliver smooth and high-quality video services for such a vehicular network. In this article, we focus on a two-hop moving network integrating both the cellular network and wireless local area network. By means of proper bandwidth reservation, we can satisfy the quality of service constraints of video applications with respect to data loss rate and packet delay. Analytical approaches are introduced to effectively estimate the achievable performance and derive the required bandwidth. To characterize video traffic, a sigmoid function is proposed to model video flows as a Markov-modulated process and fluid-flow analysis is feasible to evaluate data loss rate. At a finer packet level, the batch structure of packet arrivals is captured in the queueing analysis and packet delay is evaluated. On the other hand, for aggregate traffic multiplexed at a local gateway for the vehicular network, we use a fractional Brownian motion process to model the self-similar traffic and estimate data loss rate and packet delay. According to the performance evaluation, we can derive the required channel bandwidth for such a mobile hotspot. Numerical results are presented to demonstrate the application for bandwidth reservation, which is especially useful in case of handover.
Keywords
1 Introduction
Nowadays, mobile communications and wireless networks are ushering in a new era. The pervasive wireless networks enable ubiquitous high-rate wireless access from everywhere, such as the third-generation (3G) cellular networks, IEEE 802.16 wireless metropolitan area networks/WiMAX (worldwide interoperability for microwave access), IEEE 802.20 broadband wireless access/MobileFi, IEEE 802.11 wireless local area networks (WLAN)/Wi-Fi, and IEEE 802.15 wireless personal area networks (WPAN). With well-entrenched infrastructure, the cellular networks provide ubiquitous coverage but relatively low data rates, whereas WLANs support high data rates with cost-effective deployment over smaller geographic areas. To enable ubiquitous services, there have been intensive studies on the interworking between the complementary 3G cellular networks and WLANs by means of vertical handoff, access selection, and load balancing [1, 2]. Most of the previous studies on cellular/WLAN interworking focus on a simple scenario with static WLAN deployment in an indoor environment such as offices, hotels, and airport terminals. In addition to such slow-moving or static scenarios in an indoor (residential or business) environment, there are ever-increasing demands for systematic deployment of moving networks in a vehicular environment such as public transits (e.g., a bus, train, or airplane). For mobile hotspots in a vehicular environment, it is usually not feasible to take advantage of the overlay structure of the cellular/WLAN integrated network in an indoor environment. The high speed of vehicles poses more stringent constraints for fast, smooth, and reliable handoff. Gogo In-flight Internet by Aircell (http://www.aircell.com) is one successful story under an extremely high mobility condition, where in-flight broadband access is provided for planes flying at an altitude above 10,000 ft and at a speed of 500 miles per hour. Gogo is a ground-to-air system using the 3G cellular technology evolution-data optimized (EV-DO) integrated with 802.11 WLAN for last-hop access. However, at a high moving velocity, when the plane is crossing over ground radio towers, data rates degrade due to handoff between neighboring cells. As a result, slow playback and even halting of video streaming has been observed in previous Gogo tests [3].
It is known that mobile video will generate most of the mobile traffic growth through 2015 as predicted by Cisco [4]. The statistics collected from leading mobile operators worldwide in 2010 [5] also show video streaming accounts for 37% of mobile data usage, which is the largest part next to file sharing (30%) and Web browsing (26%). There are two major types of video services, i.e., conversational video (e.g., videoconference) and video streaming [6]. Conversational video is characterized by very stringent end-to-end latency constraint and two-way traffic with a bursty pattern due to the use of live video encoder. In contrast, video streaming usually only involves with one-way downlink traffic. Video streams can be pre-stored at application servers and allow for a pre-rolling delay of a few seconds before the start of video playback. As such, video streaming applications are more concerned with playback smoothness and the delay constraint is relaxed in a sense.
Due to high vehicular mobility, it becomes very challenging to deliver good-quality video applications for a mobile hotspot. Usually, a multi-mode wireless gateway can be deployed to connect end users in the vehicle with the backbone network. In case that a handoff is required between neighboring cells, the ongoing traffic can be multiplexed at a local gateway and handed over as a whole [7], since all end users are moving together. The multiplexing gain is thus exploited to reduce bandwidth demands. Considering the high vehicular mobility and intensive bandwidth demands of video traffic, it is essentially important to reserve bandwidth in advance for handoff traffic so as to minimize service interruptions. In order to estimate bandwidth requirement of video flows, we need to appropriately evaluate the achievable quality of service (QoS) with a given bandwidth from the two-hop vehicular network. User perceived video quality is subject to stringent QoS constraints in terms of data loss rate and packet delay.
In the literature, there has been extensive work modeling the varying data rate and frame size of video traffic [8, 9]. Traditionally, video traffic can be viewed as a fluid flow and characterized with a Markov-modulated process by neglecting traffic discreteness. To capture both frame size variation and auto-correlation, we extend the Markov-modulated Gamma-based model (MMG) proposed in [8] for performance evaluation. As discussed above, because of the group mobility feature, the ongoing traffic within a vehicular network can be multiplexed at a local gateway and handed over together. Due to the long-range dependency of video traffic, the resulting aggregate traffic exhibits self-similarity and high variability over a wide range of time scales [10]. The fractional Brownian motion (FBM) process is a powerful tool to analyze self-similar data flows [11]. Based on such traffic models, we analytically evaluate data loss rate of video traffic at the fluid-flow level and average transfer delay at the packet level for a two-hop vehicular network. Accordingly, the bandwidth requirement for a backhaul connection can be estimated to satisfy the QoS constraints of video traffic.
The rest of the article is organized as follows. Section 2 gives the two-hop relay network structure and video traffic models considered in this study. In Section 3, we introduce the approaches to analyze video performance over a two-hop wireless channel. Numerical results are presented in Section 4, followed by conclusions and future study in Section 5.
2 System model
2.1 Two-hop network structure
2.2 Video traffic model
where K is the number of video classes, Γ(·) is the Gamma function, and σ_{ k }and θ_{ k }are the shape parameter and scale parameter, respectively, for shot class k.
where δ (0 < δ < 1) is a scale factor.
where g is the arrival rate of video bursts and I is the identity matrix. Let J denote the number of B frames between two key I/P pictures and f the constant frame rate. Then, the video burst rate g = f/(J + 1).
2.3 FBM model for aggregate traffic
where m is the mean data arrival rate (the average number of data units arrived in a time interval), a is the variance coefficient (the ratio of variance to mean of data arrivals), and Z(t) is a normalized FBM process with Hurst parameter H ∈ [0.5, 1). The normalized FBM process Z(t) processes the following properties [17]: (1) Z(t) is a Gaussian process with stationary and ergodic increments; (2) Z(0) = 0 and E[Z(t)] = 0 for all t; (3) Z(t) has continuous paths; and (4) Var[Z(t)] = t^{2H}.
3 Resource reservation for aggregate handoff traffic
3.1 Video quality indicators
where PL is the data loss rate, b is a parameter that describes the motion randomness of the video scene, a is the energy loss ration of the encoder filter, ψ is the percentage of intra-coded frames, and $\mathsf{\text{E[}}\mathcal{F}\left(k,k-1\right)\mathsf{\text{]}}$ is the average value of the successive frame difference $\mathcal{F}\left(k,k-1\right)$. As seen from (7), data loss rate is an important factor determining perceived video quality.
In Figure 1, a two-hop wireless relay via heterogeneous technologies is applied for a vehicular network to facilitate ubiquitous access, alleviate power constraint, and enhance transmission rate. An end user within a vehicle is served by both a local wireless router connected with a remote cellular radio tower. In contrast to wired networks, wireless channel is highly time-varying and supports a relatively low data rate. Hence, the performance over the two-hop wireless channel contributes significantly to the end-to-end QoS. Hence, video distortion perceived at the receiver is closely related to the data loss rate over the last-hop WLAN, denoted by PL_{ w }, and that of the cellular relay channel, denoted by PL_{ c }.
where T_{ w }is the mean transfer delay over the last-hop WLAN and T_{ c }is that between the WLAN router and the cellular base station. For 802.11 WLAN, the wireless channel is shared in a contention-based access manner. It is quite limited in QoS provisioning. On the other hand, in the cellular network, reservation-based resource allocation is enabled with a centralized infrastructure. As such, the aggregate traffic of a mobile hotspot can be delivered with dedicated cellular channels. The mean transfer delay is thus dependent on the reserved bandwidth of the cellular channel.
3.2 Fluid-flow data loss rate
As shown in Figure 3, an individual video flow within the mobile hotspot is modelled by a continuous-time Markov-modulated process, whose infinitesimal generating matrix M can be obtained from video traces according to a sigmoid classification function. The data rate for each shot class k is denoted by R_{ k }(bps), which equals the average of total frame sizes of video bursts in the state over the burst duration (1/g). Without loss of generality, the shot classes (states) are arranged in an ascending order with respect to date rates R_{ k }. Viewing the video stream as a fluid flow, we can derive the equilibrium queue length distribution at the local wireless router. Let F_{ k }(x) denote the stationary probability that the buffer occupancy is less than or equal to x, given that the video source is in state k.
where $\stackrel{\u20d7}{F}\left(x\right)=\left[{F}_{1}\left(x\right),{F}_{2}\left(x\right),\dots ,{F}_{K}\left(x\right)\right]$, B is a diagonal matrix defined by B = R - C_{ L }I, R = diag[R_{1}, R_{2}, ..., R_{ k }], and M is the infinitesimal generating matrix of the underlying Markov process.
It is worth mentioning that $\stackrel{\u20d7}{{\Phi}_{k}}$ are the row eigenvectors of M ', which are equivalent to the transpose of the column eigenvectors of (M ')^{ T }, i.e., the transpose of M '. The coefficients a_{ k }can be obtained from the boundary conditions, i.e., F_{ k }(0) = 0 for R_{ k }> C_{L.}
3.3 Packet delay performance
To analyze the packet delay over the last-hop WLAN, we use a small time unit τ to discretize the time scale. The inter-arrival time of video bursts is then N = 1/(g · τ) time units, where g is the video burst rate. Given fixed packet size L, the packet transmission time over the WLAN channel is assumed to be h time units, where h = L/(C_{ L }· τ). With the aforementioned batch structure and fixed burst inter-arrival time, the average delay experienced by a tagged packet in a video batch (T_{ w }) consists of three independent components: (1) the waiting time of the first packet of that batch to be served, denoted by W_{ G }; (2) the waiting time due to the transmission of the packets of that batch queued before the tagged packet, denoted by W_{ R }; and (3) the transmission time of the tagged packet, which is assumed to be deterministic and equal to h time units.
where N is the inter-arrival time of video batches, z_{1}, ..., z_{N- 1}are the unique roots of z^{ N }- G(z) = 0 within the unit circle | z | < 1, and G'(1^{-}) and G"(1^{-}) are the first-order and second-order derivatives of G(z) at z = 1^{-}.
Based on the evaluation of data loss rate in Section 3.2 and the delay analysis this section, we can see that the achievable video quality depends on the available bandwidth within the last-hop WLAN and that of the cellular relay channel. To satisfy the QoS constraints for video transmission, we can derive the required bandwidth to be reserved for video flows within the vehicular network.
4 Numerical results and discussion
System parameters for numerical analysis
Symbol | Value | Definition |
---|---|---|
α | 1.3484 | Parameter of sigmoid function for video classification |
β | 5.2924 | Parameter of sigmoid function for video classification |
δ | 1.1 | Scale factor to segment video flows |
f | 30 | Video frame rate (/s) |
g | 3.75 | Video frame burst rate (/s) |
J | 7 | Number of B frames between two key I/P frames |
K | 6 | Number of video classes for traffic modelling |
a | 175.38 | Variance coefficient of aggregate traffic (kbit/s) |
ã | 29.23 | Variance coefficient of aggregate traffic (packets/s) |
H | 0.7 | Hurst parameter |
C _{ L } | ~3.0 | One-way channel rate of last-hop WLAN channel (Mbit/s) |
τ | 0.006 | Time unit (s) |
L | 6000 | Transmission packet size (bits) |
p | 0.4424 | Parameter of negative binomial distribution for video batch size A |
r | 2.5812 | Parameter of negative binomial distribution for video batch size A |
σ | 1.4847 | Shape parameter of Gamma distribution for video burst size G (kbits) |
θ | 27.56 | Scale parameter of Gamma distribution for video burst size G (kbits) |
4.1 Effective bandwidth for video flows
Matrix of transition probabilities for a video flow
State | State | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
1 | 0.1866 | 0.5598 | 0.2493 | 0.0044 | 0 | 0 |
2 | 0.1384 | 0.2796 | 0.5345 | 0.0468 | 0.0007 | 0 |
3 | 0.0200 | 0.1811 | 0.6670 | 0.1308 | 0.0011 | 0 |
4 | 0.0007 | 0.0267 | 0.2459 | 0.6945 | 0.0322 | 0 |
5 | 0 | 0 | 0.0227 | 0.4838 | 0.4708 | 0.0227 |
6 | 0 | 0 | 0 | 0 | 0.7778 | 0.2222 |
4.2 Bandwidth reservation to ensure fluid-flow and packet-level QoS
As introduced in Section 3.1, video distortion is one of the most direct quality indicators for video applications. It depends on various factors including encoding parameters and transmission performance such as data loss rate. Packet delay affects not only playout smoothness but also video distortion level due to dropping of expired packets. In Sections 3.2 and 3.3, we have presented the analytical approaches we can leverage to estimate the achievable performance int terms of data loss rate and packet delay. In this section, we will give some numerical examples to demonstrate the use of the above analysis to derive required channel bandwidth. It is especially useful for the the handover of a mobile hotspot between neighboring cells.
5 Conclusions and future work
In this article, we investigate the resource reservation issue for moving networks in a vehicular environment. A two-hop relay structure integrates heterogeneous technologies of the cellular network and wireless local area network. Focusing on the ever-increasing video applications, we have introduced feasible analytical approaches to effectively evaluate video performance in terms of data loss rate and packet delay. To satisfy video QoS constraints, we can derive the bandwidth to be reserved for handover. Our performance evaluation has taken into account the two-hop relay structure and the essential characteristics of video traffic. At the flow level, we use a sigmoid function to model video flows as a Markov-modulated process and calculate data loss rate with fluid-flow analysis. At a finer packet level, due to inter-coded frames, video traffic arrives in bursts and results in transmission packets as a batch for each video burst. Our queueing analysis for packet delay has considered this feature and provided fairly accurate estimate. On the other hand, video flows from end users within a vehicular network can be multiplexed at a local gateway. As the aggregate traffic exhibits self-similarity, we use a FBM process to characterize the aggregate traffic flow beyond the wireless gateway. The data loss rate and packet delay for the second hop are approximated accordingly. As demonstrated in the numerical examples, the performance evaluation can be applied to derive bandwidth requirement during handover. In our future study, we would examine the impact of varying channel rates due to radio fading and access contention. To optimize video delivery quality, we can develop an efficient handoff algorithm depending on the vehicular mobility and traffic variation.
Declarations
Acknowledgements
This research was supported by research grants from Natural Sciences and Engineering Research Council (NSERC) of Canada and New Brunswick Innovation Foundation (NBIF).
Authors’ Affiliations
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