Comparisons of the channel selection algorithms for video streaming with
The emergence of cognitive radio networks have spurred both innovative research and ongoing standards (Mitola et al., 1999; Haykin, 2005; Cordeiro et al., 2006). Cognitive radio networks have the capability of achieving large spectrum efficiencies by enabling interactive wireless users to sense and learn the surrounding environment and correspondingly adapt their transmission strategies. In this context, there exist three main challenges for multimedia users to efficiently transmit their delay-sensitive traffic over the cognitive radio networks. The first problem is how these multimedia users should sense the spectrum and timely model the behavior of the primary licensees. The second problem is how these users should manage the available spectrum resources and share the resource to the license-exempt users to satisfy their multimedia traffic requirements while not interfering with the primary licensees. The third problem is how to maintain seamless communication during the transition (hand-off) of selected frequency channels. In this chapter, we focus on the second challenge regarding the resource management problem. For the remaining two challenges, one can find relevant discussions in other existing literatures as in Akyildiz et al., 2006 and Brown, 2005, etc.
Due to the informationally-decentralized nature of cognitive radio networks (Shiang & van der Schaar, 2007b), the complexity of the optimal centralized solutions (Zekavat & Li, 2006; Fu & van der Schaar, 2007; van der Schaar & Fu, 2009) for spectrum allocation is prohibitive for large systems. In addition, the centralized solution might require a large amount of time to process and to collect the required information, which induces delay that can be unacceptable for the delay-sensitive applications, e.g. multimedia streaming. Hence, it is important to implement distributed solutions as in (Shiang & van der Schaar, 2009) for dynamic resource management by relying on the wireless users’ capabilities to sense, adapt, and coordinate themselves. Importantly, for the distributed solutions, the coordinated interactions (information exchanges) across the autonomous wireless users are essential, since the decisions of an autonomous wireless user will impact and be impacted by the other users. Without explicit coordinated interactions, the heterogeneous users will consume additional resources and respond slower to the time-varying environment. Such information exchange can rely on a dedicated control channel for all users (Brik et al., 2005), or using a group-based coordination scheme without a common control channel (Zhao et al, 2005).
In recent years, the research focus regarding multimedia transmission in wireless networks has been to adapt existing multimedia compression (Stockhammer et al., 2003), error protection algorithms (Mohr et al., 2000), and rate-distortion optimized transmission (Chou & Miao 2006) to the rapidly varying resources of wireless networks (see van der Schaar & Chou, 2007 for more references). Significant contributions have been made to enhance the separate performance of the various OSI layers, or jointly for the MAC, PHY, and application layers (van der Schaar et al., 2003; Setton et al. 2005). However, these solutions cannot provide an integrated and realistic cross-layer optimization framework in the cognitive radio networks to support delay-sensitive multimedia streaming applications. Importantly, the cross-layer optimization has been performed in an autonomous, selfish and isolated manner, at each multimedia source/user, and does not consider its impact on the overall wireless infrastructure and the interactions with other information streams. As such, existing solutions do not provide adequate support for multi-user multimedia streaming over spectrum agile network.
In this chapter, we introduce an integrated cross-layer optimization framework for multi-user multimedia transmission over cognitive radio networks. The traffic of the users (including the licensed users and the license-exempt users) and the channel conditions (e.g. Signal-to-Noise Ratio, Bit-Error-Rate) are modeled using stationary stochastic models (Shanker et al., 2005). Based on these models, a novel priority virtual queue analysis (Shiang & van der Schaar, 2008) for cognitive radio networks is introduced. This analysis enables a coordination interface to the license-exempt wireless users without requiring to change existing communication protocols, e.g. IEEE 802.22 (Cordeiro et al., 2006). The virtual queues are priority queues for each of the frequency channels. They are emulated in a distributed manner by each autonomous wireless user in order to estimate the delay of selecting a specific frequency channel for transmission. Unlike the majority of prior works assuming the available frequency channels as spectrum holes (Haykin, 2005; Akyildiz et al., 2006) that can be accessed using a 2-state on-off channel model (Shanker et al., 2005), we adopt priority queuing to model the users’ interactions by assigning the highest priority to the licensed users and adapt the channel access of the license-exempt users (with lower priorities). Importantly, instead of making the primary licensees passively exclude the license-exempt users from using the occupied frequency channels, the introduced approach allows the primary licensees to share the frequency channels and also endows these primary licensees with the preemptive priority to delay the transmissions of the license-exempt users in the same frequency channel. Hence, the introduced approach can further improve the spectrum utilization and reduce the impact of the license-exempt users. The proposed concept can also be applied to the leased network as in Akyildiz et al., 2006 and Stine, 2005.
Based on the priority queuing analysis, each wireless user builds an abstraction of the dynamic wireless environment (e.g. wireless condition) and the competing users’ behaviors using the same frequency channel (including the primary licensees, to which the highest priority is assigned). Note that the abstraction is important in order to enable intelligent wireless users to learn and adapt their cross-layer transmission strategies (Haykin, 2005; Mitola et al., 1999). Additionally, the necessary multi-agent interactions (information exchanges) are also determined for the priority queuing analysis. This chapter focuses on the delay-sensitive applications such as surveillance, multimedia conferencing, and media streaming etc., since these applications are most impacted by inefficient spectrum usage. Moreover, this chapter only focuses on the multimedia transmission over a single-hop network infrastructure. Discussion regarding to multimedia transmission over a multi-hop cognitive radio network can be found in Shiang & van der Schaar, 2009.
The organization of the chapter is as follows. In Section 2, we provide the network settings of the cognitive radio network. Section 3 presents the cross-layer problem formulation for multi-user multimedia streaming over such network through a multi-agent interaction. In Section 4, we show that the multi-user multimedia streaming problem over such network can be analyzed using priority queue modeling and hence, facilitate the optimal cross-layer transmission strategies of the multimedia streaming problem through appropriate information exchange. Finally, Section 5 concludes the chapter.
2. Network Settings for Multi-User Multimedia Transmission over Cognitive Radio Networks
2.1. Multimedia traffic characteristics
Assume that there are
, the expected quality impact of receiving the packets in the class . We prioritize the multimedia classes based on this parameter. In the subsequent part of the chapter, we label the classes (across all users) in descending order of their priorities, i.e. , the average packet lengths of the class . The expected quality improvement for receiving a multimedia packet in the class is defined as (see e.g. Wang & van der Schaar, 2006 for more details). , the number of packets in the class in one GOP duration of the corresponding multimedia sequence. , the probabilities of successfully receiving the packets in the class at the destination. Thus, the expected number of the successfully received packets of the class is , the delay deadlines of the packets in the class . Due to the hierarchical temporal structure deployed in 3D wavelet multimedia coders (see Wang & van der Schaar, 2006; van der Schaar & Turaga, 2007), for a multimedia sequence, the lower priority packets also have a less stringent delay requirement. This is the reason why we prioritize the multimedia bitstream in terms of the quality impact. However, if the used multimedia coder did not exhibit this property, we need to deploy alternative prioritization techniques that jointly consider the quality impact and delay constraints (see more sophisticated methods in e.g. Jurca & Frossard, 2007; Chou & Miao, 2006).
At the client side, the expected quality improvement for multimedia
Here, we assume that the client implements a simple error concealment scheme, where the lower priority packets are discarded whenever the higher priority packets are lost (van der Schaar & Turaga, 2007). This is because the quality improvement (gain) obtained from decoding the lower priority packets is very limited (in such embedded scalable multimedia coders) whenever the higher priority packets are not received. For example, drift errors can be observed when decoding the lower priority packets without the higher priority packets (Wang & van der Schaar, 2006). Hence, we can write:
where we use the notation in (Chou & Miao, 2006) -
2.3. Cognitive radio network settings
Assume that there are a total of
Let us further assume that there is a Network Resource Manager (NRM) that coordinates multiple access control scheme for sharing the spectrum resource (by assigning transmission opportunities), while ensuring that the corresponding interference on the primary users is eliminated. The role of the NRM is similar to the coordinator in the current IEEE 802.11e Hybrid Coordination Function (HCF) solutions for multimedia applications (van der Schaar et al., 2006). Note that the NRM will not make decisions for the secondary users, but it will only manage the transmission opportunities of the frequency channels based on the priority classes to avoid interference. In this chapter, we investigate the dynamic resource management problem for the multimedia streaming of the secondary users that are associated with this specific NRM. Primary users in the highest priority class
Multiple users can time share the same frequency channel. Note that even if the same time sharing fractions are assigned to the users choosing the same frequency channel, the experienced channel conditions can be different for the users. A wireless user needs to stream its multimedia over an appropriate frequency channel to minimize the transmission delay
The effective transmission rate
3. Cross-Layer Transmission Problem Formulation for Multi-User Multimedia Transmission over Cognitive Radio Networks
3.1. Cross-layer transmission problem formulation
The considered actions of a secondary user
The physical layer modulation and coding scheme
The MAC layer retransmission limit
The application layer multimedia packet scheduling
The selection of the frequency channel for multimedia transmissions
Denote the frequency selection of a secondary user
As stated in equation (1), each secondary user has its own multimedia quality function as the utility function to maximize. Conventionally, the utility function of a specific user is often modeled solely based on its own action, i.e.
Importantly, in an informationally-decentralized cognitive wireless network that consists of decentralized secondary users, the secondary user
Hence, we define a frequency selection strategy profile of a secondary user
Figure 1 provides the system architecture of the secondary users. In Section 4, we will discuss how to model the strategy (behavior)
3.2. Cross-layer optimization examples
We first look at the case with 6 secondary users with multimedia streaming applications (“Coastguard”, frame rate of 30Hz, CIF format, delay deadline 500ms) sharing 10 frequency channels (
|Average||"Static Assignment -Largest-Bandwidth"||"Dynamic Least Interference"||"Cross-layer O ptimization"|
|= 1 Mbps||Average Y-PSNR (dB)||Y-PSNR Standard Deviation||Average Y-PSNR (dB)||Y-PSNR Standard Deviation||Average Y-PSNR (dB)||Y-PSNR Standard Deviation|
|No p rimary users||Primary users randomly appear ()|
|Average= 1 Mbps||Average Y-PSNR (dB)||Average Y-PSNR (dB)||Y-PSNR Standard Deviation|
|"Dynamic Least Interference"||33.90||30.37||4.41|
|"Cross-layer O ptimization"||35.61||32.36||2.26|
Next, let us take a look at the impact of different numbers of secondary users with video streaming applications. Figure 2 shows the average packet loss rate and the average PSNR over the
4. Priority Queuing Analysis for Multimedia Transmission in Cognitive Radio Networks
In this section, we discuss the priority queuing analysis for the multimedia streaming problem over cognitive radio networks. The goal is to provide an abstraction of the dynamic wireless environment and the competing wireless users’ behaviors that impact the secondary user’s utility. It is important to note that the packets of the competing wireless users are physically waiting at different locations. Figure 3 gives an example of the physical queues for the case of
4.1. Traffic models of the users
Traffic model for primary users – Assume that the stationary statistics of the traffic patterns of primary users can be modeled by all secondary users. The packet arrival process of a primary user is modeled as a Poisson process with average packet arrival rate
for the primary user using the frequency channel . We denote the mth moments of the service time distribution of the primary user in frequency channel as . Note that this traffic model description is more general than a Markov on-off model (Shanker et al., 2005), which is a sub-set of the introduced queuing model with an exponential idle period and an exponential busy period. As in Shanker et al., 2005, an M/G/1 model is adopted in this chapter for the traffic descriptions.
Traffic model for secondary users – Assume that the average rate requirement for the secondary user
is (bit/s). Let denote the average packet arrival rate of the secondary user using the frequency channel . Since the strategy represents the probability of the secondary user taking action (transmitting using the frequency channel ), we have , where denotes the average packet length of the secondary user depending on which priority class of multimedia packets is chosen in . If a certain secondary user can never use the frequency channel , we fix its strategy to , and hence, . For simplicity, we also model the packet arrival process of the secondary users using a Poisson process.
Since packet errors are unavoidable in a wireless channel, let us assume that packets will be retransmitted, if they are not correctly received. This can be regarded as a protection scheme similar to the Automatic Repeat Request protocol in IEEE 802.11 networks. Hence, the service time of the users can be modeled as a geometric distribution. Let
To describe the traffic model, we define the traffic specification - for the secondary user
4.2. Queuing analysis for the secondary users with the same priority
In this subsection, we first consider the case that all packets have the same priority by ignoring the impact of the primary users. In the next subsection, we will generalize these results by considering the impact of the primary users using priority queuing.
In order to solve the dynamic resource management problem, we need to calculate the distribution of the end-to-end delay
In Figure 3, for a specific secondary user
Note that there are
Since the number of the secondary users in a regular cognitive radio network is usually large, we can approximate the virtual queue using the FIFO (First-In-First-Out) M/G/1 queuing model (i.e. when
This virtual queuing delay distribution is the service time distribution of the physical queues at the secondary users. Since the service time of the physical queue is an exponential distribution (see equation (14)), the average end-to-end delay of the secondary user
4.3. Queuing analysis with the impact of higher priority users
Based on the derivations in the previous subsection, we now consider the impact of primary users. First, let us consider the case that there are only two priority classes (i.e.
Hence, we substitute the
The derivation can be generalized to
By applying the Mean Value Analysis (MVA) (Kleinrock, 1975), we have:
Hence, for a secondary user
Therefore, we can approximate the objective function in equation (7) for the multimedia streaming of the secondary user
Note that only
We provide here an example that considers a simple network with two secondary users and three frequency channels (i.e.
|Secondary users||Physical transmission rate(Mbps)||Physical packet error rate||Rate requirement(Mbps)|
|Primary users||Normalized loading||Second moment normalized loading|
Given the statistics, Figure 5 provides the different strategy pairs
4.4. Realistic framework for multimedia transmission over cognitive radio networks using queuing analysis
The priority virtual queue analysis requires the following information to compute
Priority: the secondary users’ priorities.
Normalized loading: the secondary users’ normalized loading parameters
, which not only include the information of , but also reflects the input traffic loading and the expected transmission time using a specific frequency channel.
Variance statistics: the secondary users’ variance statistics with the normalized parameter
Hence, two kinds of information exchange are defined for the priority virtual queue analysis:
Other secondary users’ traffic specification
The frequency selection information of the other secondary users to model the strategies
Figure 6 shows the block diagram of the introduced priority virtual queue interface (Shiang & van der Schaar, 2008) together with the cross-layer optimization approach in Section 3.1. Since the traffic specification
In this chapter, we discussed the priority virtual queuing architecture for heterogeneous and autonomous secondary users in cognitive radio networks, based on which they can time share the various frequency channels in a distributed fashion. With the information exchange defined by the proposed interface, the secondary users can build an abstraction of the dynamic wireless environment as well as the competing wireless users’ behaviors and learn how to efficiently adapt their transmission strategies for multimedia streaming. Importantly, unlike conventional channel allocation schemes that select the least interfered channel merely based on the channel estimation, the introduced multi-agent priority queue modeling allows the secondary users to track the other users and adequately adapt their own transmission strategies to the changing multi-user environment. It can be shown that the introduced cross-layer optimization that applies priority queuing analysis significantly outperforms the fixed channel allocation and the current dynamic channel allocation that selects the least interfered channel, in terms of multimedia quality. Finally, we discuss the required information exchange that is required for the queuing analysis and present a realistic framework for the secondary users to transmit multimedia traffic over cognitive radio networks.
- The prioritization of the secondary users can be determined based on their applications, prices paid for spectrum access, or other mechanism design based rules. In this chapter, we will assume that the prioritization was already performed.
- The traffic specification is similar to the TSPEC in current IEEE 802.11e for multimedia transmission.