- Research Article
- Open Access
Optimal and Fair Resource Allocation for Multiuser Wireless Multimedia Transmissions
© Zhangyu Guan et al. 2009
- Received: 30 June 2008
- Accepted: 20 February 2009
- Published: 26 March 2009
This paper presents an optimal and fair strategy for multiuser multimedia radio resource allocation (RRA) based on coopetition, which suggests a judicious mixture of competition and cooperation. We formulate the co-opetition strategy as sum utility maximization at constraints from both Physical (PHY) and Application (APP) layers. We show that the maximization can be solved efficiently employing the well-defined Layering as Optimization Decomposition (LOD) method. Moreover, the coopetition strategy is applied to power allocation among multiple video users, and evaluated through comparing with existing- competition based strategy. Numerical results indicate that, the co-opetition strategy adapts the best to the changes of network conditions, participating users, and so forth. It is also shown that the coopetition can lead to an improved number of satisfied users, and in the meanwhile provide more flexible tradeoff between system efficiency and fairness among users.
- Power Allocation
- Video Quality
- Multimedia Service
- Video Transmission
- Nash Bargaining Solution
Radio resource allocation (RRA) for multimedia services has drawn a lot of attention because of its capability of offering an efficient way to handle the resources. In previous research, much attention has been paid to system efficiency improvement, that is, maximizing system utility [1–8]. It is shown that the Nash Bargaining Solution (NBS), a well-defined notion in game theory, can be used to maximize the sum of Peak Signal-to-Noise Ratios (PSNRs) in rate allocation for collaborative video transmissions . Optimal resource allocation for multiuser wireless transmissions is studied in  from an information theoretic perspective, and it is shown that sum rate maximization (SRM) is suboptimal when taking video quality into account. This work has been extended to joint power and subcarrier allocation for mutiuser video transmission in multi-carrier systems . In , Application (APP), MAC, and Physical (PHY) layers are jointly optimized using Cross-Layer Design (CLD) for streaming video delivery in a multiuser wireless environments, and two objective functions are introduced, that is, minimizing the sum of mean square error (MSE) of all video users, maximizing the sum of PSNRs. As a continuous work of [4, 5] proposed an application-driven cross-layer optimization strategy and discussed the challenges in CLD for multiuser multimedia services. Two Layering, as Optimization Decomposition (LOD) methods, dual decomposition and gradient projection-based decomposition, are used in [6, 7] for downlink utility maximization (DUM) assuming utility functions at APP layer are concave, increasing, and differentiable. The maximization of weighted sum of data rates in cross-layer resource allocation is addressed in , and an improved conjugate gradient method under given power constraint is presented as well.
In the work mentioned above, all the resource allocation methods try to maximize the global utility function. There are also several resource allocations that run in a distributive way, for instance, ReSerVation Protocol (RSVP) was used to allocate bandwidth among multiple multimedia streams over internet based on the Traffic SPECifications (TSPECs) ; air time fairness allocates transmission time proportionally to TSPECs to eliminate the passive impact of cross-layer strategies employed in different transmitters . Proportional fairness was introduced  to allocate resources based on users' rate requirements, and further applied to rate controlling . In , the Kalai-Smorodinsky Bargaining Solution (KSBS) was used to allocate rates amongst multiple video users such that the utility achieved by each user is proportional to the maximum utility achievable.
Both maximization based and distributive policies work in a competitive way as explained by the following two examples. Utility maximization can actually be viewed as a process in which all users compete for resources according to the criteria that the Highest Quality Improvement the Highest Possibility Resources (HQIHPR) . Using KSBS, users compete for resources to make efficient use of the resource and achieve higher utility. The disadvantage of these competitive policies is that they do not consider user's quality of service (QoS) satisfication degree, meaning that they are not suitable for multimedia services. To address this disadvantage, we propose an optimal and fair policy for multimedia resource allocation, which introduces a judicious mixture of competition and cooperation, such that user's QoS satisfication degree is taken into account. The idea behind this judicious mixture is Co-opetition, a concept from economic . Co-opetition has been employed in decentralized resource management  and collaborative multimedia resource allocation in our preliminary work . It is shown that co-opetition can provide better tradeoff between system efficiency and fairness.
Main contribution of this paper relies on the proposal of a novel co-opetition strategy for RRA in multimedia services, which is both optimal and fair. In this paper, optimal represents sum utility maximization (SUM) subject to the constraints on individual utility. It is worth to mention that the value of optimal sum utility might be smaller than that achieved by the unconstrained SUM, due to the constraints. Fair is defined to describe that, compared to unconstrained SUM, our strategy can result in fairer resource allocation. The additional fairness from our strategy comes from the individual utility constraint. Recall that the unconstrained SUM allocates resources in a competitive way, which has no constraint on individual utility. Our co-opetition strategy suggests a judicious mixture of competition and cooperation in resource allocation. We formulate the co-opetition strategy mathematically and solve it efficiently using LOD method. This mathematical formulation would help to get a better insight into the essential of competition and cooperation behaviors of users in RRA. We apply our strategy to wireless resource allocation for multiuser video transmissions and evaluate its performance by comparing with existing competition based mechanisms.
The rest of this paper is organized as follows. In Section 2, we formulate the co-opetition strategy, and in Section 3 we implement it by employing LOD method. In Section 4, we apply the co-opetition strategy to power allocation amongst multiple video users together with numerical results for performance evaluation. Conclusion is drawn in Section 5.
We consider RRA over a downlink transmission with users. We assume that the resource available at PHY layer is denoted by . Denote as the rate region achievable at PHY layers, and assume that is convex and compact. Convexity assumption means that time-sharing mode is enabled at PHY layer. Let denote the user 's utility function, which is assumed to be concave, increasing, and differentiable. An example of utility is PSNR for video services . Each user has a minimum desired rate, denoted by , which should be at least guaranteed. That means
otherwise, user would not be served. A competition strategy should be employed to develop our co-opetition strategy. In this paper, we focus on optimization-based strategy, that is, sum utility maximization (SUM). Investigation based on distributive and competition-based strategies will be accommodated in our future work. For SUM, system utility function is defined as
where . Hence, SUM can be written as
To allow co-opetition, we first define the notion of satisfied user. A user is called satisfied user if its achieved QoS is above or equal to predefined QoS threshold, . Then the basic idea of co-opetition can be described as follows. During the process of RRA, in which all users compete for resources to achieve SUM, users who have achieved stop competing temporarily, until all resources have been allocated or all users have been satisfied. Denote rate required by user to achieve with , and denote as . We distinguish the following two cases.
(1)If , co-opetition allocates resources such that the minimum utility of all users is , that is, .
(2)If , co-opetition allocates resources such that the maximum utility of all users is , that is, .
Thus, our co-opetition strategy reads
Introducing provides better tradeoff between system efficiency and fairness. For example, for video services in which PSNR is chosen as a QoS metric, can be set corresponding to dB, above which user could achieve good video quality and user's video satisfaction degree increases very slowly as PSNR increases. In this case, rate, which can translate to resources at PHY layer, is more important to unsatisfied users. In the following, we investigate how the LOD method is used to solve (4) efficiently.
LOD is a well-defined technique for network utility maximization (NUM) by decomposing the NUM into a set of subproblems coupled with each other. Each subproblem is associated with a protocol layer, in which it can be solved separately .
3.1. Rewrite Co-Opetition Strategy
We assume it is known whether can be achieved or not. In the case of , translates into , and translates into otherwise. We also assume that
always satisfies. Then constraints in (4) can be rewritten as
where (In the case of , total resource available cannot guarantee all users the minimum resource required, and some users will deny to be served. In this paper, we assume the minimum resource of all users can be always guaranteed, that is, .). We observe that, no matter or not, the constraint has the same form of
with . Hence, (4) can be rewritten as
3.2. Dual Decomposition
To solve (8) with LOD, (8) is firstly modified by introducing an additional variable , then the primal function (8) reads
After introducing the Lagrangian factors
the Lagrangian function of (9) is written as
with . Thus, the dual function is
The maximization in (9) can be solved by searching the optimum and such that the dual function is minimized, that is,
Based on the analysis afore, (A.2) can be decomposed into two subproblems as
For given and , the above two-maximization can be solved independently at APP layer for (15) and at PHY layer for (16). So far, we have transformed the original maximization, (8), into its dual problem.
3.3. Solving (13), (15) and (16)
As mentioned above, for each fixed and , (15) and (16) have to be solved. Denote as the item to be maximized in (15), that is,
Then is continuous and differentiable, and further denote as set of such that
Then (15) can be solved via efficiently selecting the optimum , such that
Maximization of (16) refers to weighted sum rate maximization (WSRMax) at constraint of maximizing individual rate for certain PHY layer setup. is a general constraint usually corresponding to given power or bandwidth. can be translated into individual constraint. Recall that, is assumed to be convex and compact, thus the domain of (16), denoted with ,
Hereafter, we assume that for each and , (15) and (16) can be solved efficiently. Then the optimum and can be determined, for example, using either sub-gradient method, cutting plane method or ellipsoid method . In Section 5, we would show how to solve (13), (15) and (16) more concretely through power allocation.
3.4. Determining whether or Not
Note that is not necessarily achievable. Whether or not can be determined by userwisely computing the minimum resource required to achieve . Fortunately again there are several solutions available for different scenarios. For example, in  a generic procedure, CLARA, was presented for cross-layer resource minimization subject to a set of constraints on the overall QoS.  proposed an iterative algorithm which monotonically converges to the unique allocation with optimal sum power efficiency. This is actually another hot topic as opposed to utility maximization in this paper, namely, cost minimization to achieve certain QoS.
3.5. Summery of LOD Method
In this Section, we first describe the system scenario, and then illustrate the co-opetition strategy in detail. Finally, numerical results are presented for performance evaluation through comparing with competition-based strategy.
4.1. System Setup
We consider downlink -user video transmission in a cell with a base-station (BS) which acts as the central spectrum manager (CSM). At APP layer, users transmit same or different video sequences. We choose PSNR as user's utility as it is the only widely accepted video QoS metric and choose the rate-distortion (RD) model proposed in  to describe user's average RD behavior as this model applies well to the state-of-the-art video encoder . Then user's utility can be defined as
where and are sequence parameters, which are dependent on video sequence characteristics, such as spatial and temporal resolution, delay constraints as well as the percentage of INTRA coded macro-blocks [1, 16]. is the minimum rate that should be at least guaranteed for user , therefore in this work we assume that .
At PHY layer, the BS has limited transmit power, . Let represent the power allocated to all the users, thus we have . Each user is assumed to experience an AWGN channel, whose capacity, , is given by
where and denote bandwidth available and receiver noise power, respectively.
It is assumed that private information of each user, including , are sent to CSM, where power allocation is made. Then CSM sends back the decision of power allocated to each user. Note that, more complicated PHY layer setups can also be taken into account, such as multicarrier and multiple antennas systems over Rayleigh fading channels. However, employing simple PHY layer setup would help to highlight the focus of this paper, investigating optimal and fair criteria for RRA. It is worth mentioning that the co-opetition strategy can be easily extended to other scenarios.
4.2. Co-Opetition Strategy
4.2.1. Co-Opetition Formulation
According to the common sense in the field of video signal processing, the PSNR threshold can be set to different values, such as 40 dB, 35 dB, or 32 dB, representing perfect, good and acceptable video quality, respectively. The PSNR threshold can also be set dynamically according to the total resources available, the number of users, and so forth. As an illustration, we choose QoS threshold as PSNR = 35 dB corresponding to good video quality, that is, dB in (4). Denote as representing power required by users to achieve PSNR of 35 dB. Using co-opetition strategy, if ( means calculating the sum of all members in , i.e., .), the lower and upper bounds of achievable PSNR are set at dB and , respectively, and and dB otherwise. Correspondingly, when we have , lower and upper bounds of rates are and , respectively, and and otherwise. In this paper, it is easy to calculate corresponding to PSNR threshold, for both (21) and (22) are invertible and monotonic increasing functions. Thus, given PSNR threshold, or not can be easily determined, and consequently, both and are known.
Given each user's utility definition in (21) and (22), system utility writes
where refers to as . We assume that capacity approaching channel codes is employed at PHY layer. Then our co-opetition strategy writes
where . Note that (24) has the same form as (8). The first constraint on the sum of the power (24) corresponds to in (8).
4.2.2. The Implement of Co-Opetition
where , and
where is defined as the upper bound of transmit power of user corresponding to .
The optimum variable of (25), , can be obtained by simply making the partial derivative of and let it equal to 0,
Then we have
As mentioned in Section 3.3, (26) can be solved at PHY layer by the weighted sum rate maximization with the constraints of total and individual power. Note that in (22) is concave and increasing with respect to , thus the item to be maximized in (26) is also concave increasing. The domain of (26) is formed by two linear inequalities, each of which forms a convex domain together with . Thus the domain of (26) is also convex, and (26) is accessible to conventional convex optimization techniques, such as feasible direction method and projected gradient method. In this paper the feasible increasing direction method is employed (see the Appendix for details).
So far, given fixed , two subproblems, (25) and (26), have been solved. We denote the optimal values of them with and , respectively. In the following, the optimum , denoted by , will be determined such that the sum of and is minimized, that is,
Note that, the dual function might not be differentiable or, in other words, (29) is not accessible to classical computational method, such as steepest descent method. In this paper we employ the sub-gradient method, which applies to both differentiable and nondifferentiable dual functions. Much like the feasible increasing direction method, sub-gradient method also searches the optimal and iteratively. The main iteration writes
where is the step-size which can be set as constant, and denotes the sub-gradient at . Note that, at rightly forms a sub-gradient, so the sub-gradient can be obtained almost without any cost.
4.3. Numerical Results
In this subsection, the proposed co-opetition strategy (co-opetition) is evaluated by comparing with the strategy proposed in , which allocates resources using the Nash bargaining Solution of Same bargaining Power (NBS_SP). For the sake of comparison, we use the same test sequences as those in , and we list the parameters in Table I for reader's convenience.
4.3.1. Comparison in Terms of Individual PSNR
4.3.2. Comparison in Terms of the Number of Satisfied Users and Minimum PSNRs
test video sequences (videoID, video type, temporal level (TL), frame rate).
Foreman (CIF, , 30 Hz)
Coastguard (CIF, , 30 Hz)
Mobile (CIF, , 30 Hz)
Foreman (QCIF, , 30 Hz)
Foreman (CIF, , 15 Hz)
Foreman (CIF, , 30 Hz)
4.3.3. Adaptive Co-Opetiton Strategy
In previous experiments, the threshold PSNR is fixed to be 35 dB. In order to consider more fairness in resource allocation, adaptive threshold can be employed. As an illustration, we present a simple method to set the threshold PSNR. More optimal and fair scheme for determining the threshold PSNR will be investigated in our future work. We employ PSNR = 32 dB, 34 dB and 36 dB to represent acceptable, good and very good quality, respectively. Denote resources required by the three levels with , then threshold PSNR, , can be determined as follows
where is denote as total resources available.
4.3.4. Optimality Verification
From these two figures, we can see that our strategy is optimal under individual constraints. In Figure 2, cannot satisfy two users simultaneously. Therefore the PSNR of user 1 is pegged at the threshold PSNR = 35 dB. The optimal average PSNR can be achieved after 14 iterations. In Figure 5(b), can make satisfying PSNR for both the two users. We observe that, user 2's PSNR has only little fluctuation, and converges to the threshold. At the optimal power allocation, both the two users' PSNRs are above or equal to the threshold. All these coincide with the results in Figure 2.
To summarize, threshold PSNR plays importantly in adaptive/nonadaptive co-opetition strategies. It provides radio resource allocation (RRA) with more flexible tradeoff between system efficiency and fairness among users.
We formulate the co-opetition strategy as sum utility maximization under constraints from both APP and PHY layers. APP layer constraints imply that co-opetition takes the QoS satisfaction degree into account in RRA.
We show that the co-opetition strategy can be implemented efficiently through applying the LOD method. Therefore the co-opetition strategy can easily apply to real time multimedia services.
We apply the co-opetition strategy to power allocation among multiple video users. Numerical results indicate that co-opetition can result in an improved number of satisfied users and significant improvement in minimum PSNRs as well. A simple method for adaptively determining threshold PSNR is also presented, such that fairer resource allocation can be achieved.
We conclude that co-opetition, that is, mixture of cooperation and competition, is more applicable to multiuser multimedia RRA than pure competition based strategy. Co-opetition strategy is not only optimal, but also fair.
Our future work is to design more feasible co-opetition strategy for different system setups, including multicarrier and multiple antennas systems. We also wish to extend our preliminary work to future heterogenous network, in which users not necessarily run in a collaborative way.
Algorithm 1: Feasible increasing direction method.
1: Set and , Precision
2: Determine using(A.1)
3: Determine according (A.4) and(A.5)
4: Determine using(A.6)
5: Compute using(A.8)
This work was supported by NSFC (No. 60672036, No. 60832008) and Key Project of Provincial Scientific Foundation of Shandong (No. Z2008G01).
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