Zoomable video streaming refers to a new class of interactive video applications, where users can zoom into a video stream to view a selected region of interest in higher resolutions and pan around to move the region of interest. The zoom and pan effects are typically achieved by breaking the source video into a grid of independently decodable tiles. Streaming the tiles to a set of heterogeneous users using broadcast is challenging, as users have different link rates and different regions of interest at different resolution levels. In this paper, we consider the following problem: given the subset of tiles that each user requested, the link rate of each user, and the available time slots, at which resolution should each tile be sent, to maximize the overall video quality received by all users. We design an efficient algorithm to solve the problem above, and evaluate the solution on a testbed using 10 mobile devices. Our method is able to achieve up to 12dB improvements over other heuristic methods.
To further improve streaming performance, the last category of research jointly adapts video data rate and multicast link rate. This category is the most related to our work. @cite_13 investigate the utility optimization problem of scalable video multicast and prove that this problem is NP-Hard. A greedy algorithm is then proposed to schedule the transmissions of layers and determine the corresponding modulation and coding scheme (MCS) assigned for each transmission. @cite_24 suggest a pseudo-polynomial algorithm with dynamic programming to solve the optimization problem. Most recently, MuVi @cite_23 has been designed to investigate the optimal multicast scheduling problem for videos encoded with I, P, and B frames. As the computational complexity of the suggested algorithm @cite_24 grows quadratically with the number of available time slots, it fails to efficiently solve the optimization problem with multiple multicast sessions. To reduce the computational complexity especially for the case of multiple sessions, a fully polynomial time approximation algorithm is presented @cite_0 . The approximation factor, however, linearly decreases with the number of multicast sessions.
@cite_24 @cite_0 @cite_13 @cite_23
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Scalable video coding (SVC), together with adaptive modulation and coding (AMC), can improve wireless multicast streaming video by jointly performing radio resource allocation and modulation and coding scheme (MCS) selection. However, the existing schemes in the literature allocate radio resources for different video layers separately, which leads to a waste of radio resources. In this work, we introduce the notion of joint layer resource allocation which allows to jointly allocate resources to multiple video layers that are assigned the same MCS. We formulate this problem and prove it to be NP-hard. Then we develop a pseudo-polynomial algorithm that finds the optimal total system utility. Our algorithm assumes a very generic utility function and flexible video layer rates. To reduce the complexity of the algorithm, we also propose Fully Polynomial Time Approximation Schemes (FPTAS) for the same problem. Simulation results show that our optimal algorithm offers significant improvement on system utility over a previous optimal algorithm and a greedy algorithm both of which do not support joint layer resource allocation. The proposed approximation algorithm provides controllable tradeoff between performance and computational complexity and, with appropriately chosen parameters, it outperforms the greedy algorithm with 40 less running time. Future 4G cellular networks are featured with high data rate and improved coverage, which will enable realtime video multicast and broadcast services. Scalable video coding is very appropriate for wireless multicast service because it allows to choose different modulation and coding schemes (MCSs) for different video layers and can provide good video quality to users in good channel conditions while still maintaining reasonable video quality for other users. In order to apply scalable video coding to wireless multicast streaming, it is important to choose appropriate MCS for each video layer and to determine the right resource allocation among multiple video sessions. In this paper we propose a two-step dynamic programming algorithm that finds the optimal total system utility of all users where the system utility can be a generic non-negative, non-decreasing function of received rate. Our algorithm supports variable layer size and thus allows flexibility during the video encoding process. Simulation results show that our algorithm offers significant improvement on the video quality over a naive algorithm and an adapted greedy algorithm, especially in the scenarios with multiple real video sequences and limited radio resources. With the constraints of network topologies and link capacities, achieving the optimal end-to-end throughput in data networks has been known as a fundamental but computationally hard problem. In this paper, we seek efficient solutions to the problem of achieving optimal throughput in data networks, with single or multiple unicast, multicast and broadcast sessions. Although previous approaches lead to solving NP-complete problems, we show the surprising result that, facilitated by the recent advances of network coding, computing the strategies to achieve the optimal end-to-end throughput can be performed in polynomial time. This result holds for one or more communication sessions, as well as in the overlay network model. Supported by empirical studies, we present the surprising observation that in most topologies, applying network coding may not improve the achievable optimal throughput; rather, it facilitates the design of significantly more efficient algorithms to achieve such optimality. In this paper, we investigate the problem of Multicast Routing in Sparse Splitting Networks (MR-SSN). Given a network topology with the multicast capable nodes distributed uniformly throughout the network, and a multicast session, the MR-SSN problem is to find a route from the source node of the multicast session to all destinations of the multicast session such that the total number of fibers used in establishing the session is minimized. In this paper, we develop a rerouting algorithm for a given Steiner tree, which makes it feasible to route a multicast session using a tree-based solution in sparse light splitting optical networks. In addition, we present a heuristic based on Tabu Search (TS) that requires only one transmitter for the source node and one wavelength for each multicast session. To evaluate the performance of heuristics, we formulate the MR-SSP problem as an integer linear program (ILP), and optimally solve small instances using the commercially available linear solver, CPLEX. We test our heuristic on a wide range of network topologies. Our experimental results show that: (1) The difference between our solution and ILP optimal solution, in terms of the number of fibers used for establishing a multicast session, is within 10 in almost all the instances and within 5 in about half of the instances. (2) The average delay, taken over all destination nodes, falls within three times the optimal value. (3) A sparse light splitting all-optical network with 30 of multicast capable cross-connects has an acceptable low cost and relatively good performance. (4) The improvement achieved by TS heuristic increases considerably when the session size is large, the number of Splitter-and-Delivery cross-connects is small, and the network connectivity is dense. In this paper we consider the optimal design and provisioning of WDM networks for the grooming of multicast subwavelength traffic. We develop a unified framework for the optimal provisioning of different practical scenarios of multicast traffic grooming. We also introduce heuristic solutions. Optimal solutions are designed by exploiting the specifies of the problems to formulate Mixed Integer Linear Programs (MILPs). Specifically, we solve the generic multicast problem in which, given a set of multicast sessions and all destination nodes of a multicast session requiring the same amount of traffic, all demands need to be accommodated. The objective is to minimize the network cost by minimizing the number of higher layer electronic equipment and, simultaneously, minimizing the total number of wavelengths used. We also solve two interesting and practical variants of the traditional multicast problem, namely, multicasting with partial destination set reachability and multicasting with traffic thinning. For both variants, we also provide optimal as well as heuristic solutions. Also, the paper presents a number of examples based on the exact and heuristic approaches Scalable reliable multicast protocols have been the focus of recent research, tackling the problem of efficient reliable data delivery to an arbitrarily large number of receivers. Yet, the common applications of multicast, such as multi-point file delivery, or video streaming from a media server, typically only involve a moderate number of receivers, such as a thousand or fewer. Moreover, because of the limited deployment of these specialized multicast protocols, it is common, when feasible, for applications to use multiple TCP connections instead, one for each receiver, to implement multi-point delivery, causing a significant demand on the transmission server and the downstream links. We describe a multicast extension to TCP, called single-source multicast optimization (SMO), that optimizes this case of multipoint delivery, providing the benefits of multicast together with the familiar features and API of TCP. Our results from experiments based on a Linux implementation and performed on a testbed show that TCP-SMO requires just a modest extension to the TCP implementation and provides scalable performance of multicast up to over a thousand receivers, thereby satisfying the common case requirements. In addition, used with TCP-RTM (real-time mode), TCP-SMO also supports real-time multimedia multicast applications well. In this paper, we concentrate on opportunistic scheduling for multicast information. We pose the problem as a multicast throughput optimization problem. As a solution we present how one can jointly utilize fixed-rate and rateless erasure coding along with simple rate adaptation techniques in order to achieve the optimal multicast throughput per user. We first investigate the performance of the proposed system under i.i.d. channel conditions. Our analysis shows a linear gain for the multicast capacity over i.i.d. Rayleigh fading channels with respect to the number of users. Since the established results require coding over large number of blocks and hence induce large decoding delays, we extend our analysis to the cases where we code over shorter block lengths and thus quantify the delay-capacity tradeoffs under a simple setting. We further look into non-i.i.d. channel conditions and show achievable gains by modifying a scheduling heuristic whose fairness is well- established for opportunistic scheduling of unicast flows. Our overall evaluations demonstrate that under both i.i.d. and non-i.i.d. channel conditions, opportunistic multicasting with erasure coding can significantly improve the performance over the traditional techniques used in today's communication systems. With the advent of next-generation, bandwidth-intensive multimedia applications such as HDTV, interactive distance learning, and movie broadcasts from studios, it is becoming imperative to exploit the enormous bandwidth promised by the rapidly growing wavelength-division-multiplexing (WDM) technology. These applications require multicasting of information from a source to several destination nodes which should be performed judiciously to conserve expensive network resources. In this study, we investigate two switch architectures to support multicasting in a WDM network: one using an opaque (optical-electronic-optical approach and the other using a transparent (all-optical) approach. For both these switch architectures, we present mathematical formulations for routing and wavelength assignment of several light-tree-based multicast sessions on a given network topology at a globally optimal cost. We expand our work to also accommodate: 1) fractional-capacity sessions (where a session's capacity is a fraction of a wavelength channel's bandwidth, thereby leading to "traffic-groomed" multicast sessions) and 2) sparse splitting constraints, i.e., limited fanout of optical splitters and limited number of such splitters at each node. We illustrate the solutions obtained on different networks by solving these optimization problems, which turn out to be mixed integer linear programs (MILPs). Because the MILP is computationally intensive and does not scale well for large problem sizes, we also propose fast heuristics for establishing a set of multicast sessions in a network with or without wavelength converters and with fractional-capacity sessions. We find that, for all scenarios, the heuristics which arrange the sessions in ascending order with respect to destination set size and or cost perform better in terms of network resource usage than the heuristics which arrange the sessions in descending order. This paper shows that, for single-hop WDM networks, a multicast scheduling algorithm which always tries to partition a multicast transmission into multiple unicast or multicast transmissions may not always produce lower mean packet delay than a multicast scheduling algorithm which does not partition multicast transmissions. The performance of a multicast scheduling algorithm may depend on the traffic conditions and the availability of the channel resource in the network. A hybrid multicast scheduling algorithm that can produce good performance for wide ranges of the traffic conditions and the availability of the channel resource in the network is proposed. Depending on the average utilizations of the data channels and the receivers, the proposed hybrid multicast scheduling algorithm dynamically chooses to employ a multicast scheduling algorithm which always tries to partition multicast transmissions or a multicast scheduling algorithm which does not partition multicast transmissions. Extensive simulations are performed to study the performance of the proposed hybrid algorithm. Our simulation results show that the proposed hybrid algorithm produces lower mean packet delay for wide ranges of the load, the maximum multicast group size, the percentage of unicast traffic, and the number of data channels in the network compared with a multicast scheduling algorithm which always tries to partition multicast transmissions and a multicast scheduling which does not partition multicast transmissions. As wavelength division multiplexing (WDM) technology matures and multicast applications become increasingly popular, supporting multicast at the WDM layer becomes an important and yet challenging topic. In this paper, we study constrained multicast routing in WDM networks with sparse light splitting, i.e., where some switches are incapable of splitting light (of copying data in the optical domain) due to evolutional and or economical reasons. Specifically, we propose four WDM multicast routing algorithms, namely, re-route-to-source, re-route-to-any, member-first, and member-only. Given the network topology, multicast membership information, and light splitting capability of the switches, these algorithms construct a source-based multicast "light-forest" (consisting one or more multicast trees) for each multicast session. While the first two algorithms can build on a multicast tree constructed by IP (which does not take into consideration the splitting capability of the WDM switches), the last two algorithms attempt to address the joint problem of optimal multicast routing and sparse splitting in WDM networks. The performance of these algorithms are compared in terms of the average number of wavelengths used per forest (or multicast session), average number of branches involved (bandwidth) per forest as well as average number of hops encountered (delay) from a multicast source to a multicast member. The results obtained from this research should present new and exciting opportunities for further theoretical as well as experimental work. We study the problem of wavelength assignment for multicast in order to maximize the network capacity in all-optical wavelength-division multiplexing networks. The motivation behind this work is to minimize the call blocking probability by maximizing the remaining network capacity after each wavelength assignment. While all previous studies on the same objective concentrate only on the unicast case, we study the problem for the multicast case. For a general multicast tree, we prove that the multicast wavelength assignment problem of maximizing the network capacity is NP-hard and propose two efficient greedy algorithms. We also study the same problem for a special network topology, a bidirectional ring network, which is practically the most important topology for optical networks. For bidirectional ring networks, a special multicast tree with at most two leaf nodes is constructed. Polynomial time algorithms for multicast wavelength assignment to maximize the network capacity exist under such a special multicast tree with regard to different splitting capabilities. Our work is the first effort to study the multicast wavelength assignment problem under the objective of maximizing network capacity. This paper deals with the design of efficiently computable incentive-compatible mechanisms for combinatorial optimization problems with single-minded agents each possibly having multiple private parameters. We focus on approximation algorithms for NP-hard mechanism design problems. These algorithms need to satisfy certain monotonicity properties to ensure truthfulness. Since most of the known approximation techniques do not fulfill these properties, we study alternative techniques. Our first contribution is a quite general method to transform a pseudopolynomial algorithm into a monotone fully polynomial time approximation scheme (FPTAS). This can be applied to various problems like, e.g., knapsack, constrained shortest path, or job scheduling with deadlines. For example, the monotone FPTAS for the knapsack problem gives a very efficient, truthful mechanism for single-minded multiunit auctions. The best previous result for such auctions was a 2-appro-xi-ma-tion. In addition, we present a monotone PTAS for the generalized assignment problem with any constant number of private parameters per agent. The most efficient way to solve packing integer programs (PIPs) is linear programming-based randomized rounding, which also is in general not monotone. We show that primal-dual greedy algorithms achieve almost the same approximation ratios for PIPs as randomized rounding. The advantage is that these algorithms are inherently monotone. This way, we can significantly improve the approximation ratios of truthful mechanisms for various fundamental mechanism design problems like single-minded combinatorial auctions (CAs), unsplittable flow routing, and multicast routing. Our primal-dual approximation algorithms can also be used for the winner determination in CAs with general bidders specifying their bids through an oracle. We consider the problem of throughput-optimal scheduling in wireless networks subject to interference constraints. We model the interference using a family of K -hop interference models. We define a K-hop interference model as one for which no two links within K hops can successfully transmit at the same time (Note that IEEE 802.11 DCF corresponds to a 2-hop interference model.) .For a given K, a throughput-optimal scheduler needs to solve a maximum weighted matching problem subject to the K-hop interference constraints. For K=1, the resulting problem is the classical Maximum Weighted Matching problem, that can be solved in polynomial time. However, we show that for K>1,the resulting problems are NP-Hard and cannot be approximated within a factor that grows polynomially with the number of nodes. Interestingly, we show that for specific kinds of graphs, that can be used to model the underlying connectivity graph of a wide range of wireless networks, the resulting problems admit polynomial time approximation schemes. We also show that a simple greedy matching algorithm provides a constant factor approximation to the scheduling problem for all K in this case. We then show that under a setting with single-hop traffic and no rate control, the maximal scheduling policy considered in recent related works can achieve a constant fraction of the capacity region for networks whose connectivity graph can be represented using one of the above classes of graphs. These results are encouraging as they suggest that one can develop distributed algorithms to achieve near optimal throughput in case of a wide range of wireless networks. One central issue in practically deploying network coding is the adaptive and economic allocation of network resource. We cast this as an optimization, where the net-utility-the difference between a utility derived from the attainable multicast throughput and the total cost of resource provisioning-is maximized. By employing the MAX of flows characterization of the admissible rate region for multicasting, this paper gives a novel reformulation of the optimization problem, which has a separable structure. The Lagrangian relaxation method is applied to decompose the problem into subproblems involving one destination each. Our specific formulation of the primal problem results in two key properties. First, the resulting subproblem after decomposition amounts to the problem of finding a shortest path from the source to each destination. Second, assuming the net-utility function is strictly concave, our proposed method enables a near-optimal primal variable to be uniquely recovered from a near-optimal dual variable. A numerical robustness analysis of the primal recovery method is also conducted. For ill-conditioned problems that arise, for instance, when the cost functions are linear, we propose to use the proximal method, which solves a sequence of well-conditioned problems obtained from the original problem by adding quadratic regularization terms. Furthermore, the simulation results confirm the numerical robustness of the proposed algorithms. Finally, the proximal method and the dual subgradient method can be naturally extended to provide an effective solution for applications with multiple multicast sessions Multicast is an efficient means of transmitting the same content to multiple receivers while minimizing network resource usage. Applications that can benefit from multicast such as multimedia streaming and download, are now being deployed over 3G wireless data networks. Existing multicast schemes transmit data at a fixed rate that can accommodate the farthest located users in a cell. However, users belonging to the same multicast group can have widely different channel conditions. Thus existing schemes are too conservative by limiting the throughput of users close to the base station. We propose two proportional fair multicast scheduling algorithms that can adapt to dynamic channel states in cellular data networks that use time division multiplexing: inter-group proportional fairness (IPF) and multicast proportional fairness (MPF). These scheduling algorithms take into account (1) reported data rate requests from users which dynamically change to match their link states to the base station, and (2) the average received throughput of each user inside its cell. This information is used by the base station to select an appropriate data rate for each group. We prove that IPF and MPF achieve proportional fairness among groups and among all users inside a cell respectively. Through extensive packet-level simulations, we demonstrate that these algorithms achieve good balance between throughput and fairness among users and groups. Multicasting is emerging as an enabling technology for multimedia transmissions over wireless networks to support several groups of users with flexible quality of service (QoS) requirements. Although multicast has huge potential to push the limits of next generation communication systems; it is however one of the most challenging issues currently being addressed. In this survey, we explain multicast group formation and various forms of group rate determination approaches. We also provide a systematic review of recent channel-aware multicast scheduling and resource allocation (MSRA) techniques proposed for downlink multicast services in OFDMA based systems. We study these enabling algorithms, evaluate their core characteristics, limitations and classify them using multidimensional matrix. We cohesively review the algorithms in terms of their throughput maximization, fairness considerations, performance complexities, multi-antenna support, optimality and simplifying assumptions. We discuss existing standards employing multicasting and further highlight some potential research opportunities in multicast systems. We study downlink beamforming in a single-cell network with a multi-antenna base station (BS) serving cache-enabled users. For a given common rate of the files in the system, we first formulate the minimum transmit power with beamforming at the BS as a non-convex optimization problem. This corresponds to a multiple multicast problem, to which a stationary solution can be efficiently obtained through successive convex approximation (SCA). It is observed that the complexity of the problem grows exponentially with the number of subfiles delivered to each user in each time slot, which itself grows exponentially with the number of users in the system. Therefore, we introduce a low-complexity alternative through time-sharing that limits the number of subfiles that can be received by a user in each time slot. It is shown through numerical simulations that, the reduced-complexity beamforming scheme has minimal performance gap compared to transmitting all the subfiles jointly, and outperforms the state-of-the-art low-complexity scheme at all SNR and rate values with sufficient spatial degrees of freedom, and in the high SNR high rate regime when the number of spatial degrees of freedom is limited. Optimized opportunistic multicast scheduling (OMS) is studied for cellular networks, where the problem of efficiently transmitting a common set of fountain-encoded data from a single base station to multiple users over quasi-static fading channels is examined. The proposed OMS scheme better balances the tradeoff between multiuser diversity and multicast gain by transmitting to a subset of users in each time slot using the maximal data rate that ensures successful decoding by these users. We first analyze the system delay in homogeneous networks by capitalizing on extreme value theory and derive the optimal selection ratio (i.e., the portion of users that are selected in each time slot) that minimizes the delay. Then, we extend results to heterogeneous networks where users are subject to different channel statistics. By partitioning users into multiple approximately homogeneous rings, we turn a heterogeneous network into a composite of smaller homogeneous networks and derive the optimal selection ratio for the heterogeneous network. Computer simulations confirm theoretical results and illustrate that the proposed OMS can achieve significant performance gains in both homogeneous and heterogeneous networks as compared with the conventional unicast and broadcast scheduling. We present decentralized algorithms that compute minimum-cost subgraphs for establishing multicast connections in networks that use coding. These algorithms, coupled with existing decentralized schemes for constructing network codes, constitute a fully decentralized approach for achieving minimum-cost multicast. Our approach is in sharp contrast to the prevailing approach based on approximation algorithms for the directed Steiner tree problem, which is suboptimal and generally assumes centralized computation with full network knowledge. We also give extensions beyond the basic problem of fixed-rate multicast in networks with directed point-to-point links, and consider the case of elastic rate demand as well as the problem of minimum-energy multicast in wireless networks. The problem of transmit beamforming to multiple cochannel multicast groups is considered, when the channel state is known at the transmitter and from two viewpoints: minimizing total transmission power while guaranteeing a prescribed minimum signal-to-interference-plus-noise ratio (SINR) at each receiver; and a "fair" approach maximizing the overall minimum SINR under a total power budget. The core problem is a multicast generalization of the multiuser downlink beamforming problem; the difference is that each transmitted stream is directed to multiple receivers, each with its own channel. Such generalization is relevant and timely, e.g., in the context of the emerging WiMAX and UMTS-LTE wireless networks. The joint problem also contains single-group multicast beamforming as a special case. The latter (and therefore also the former) is NP-hard. This motivates the pursuit of computationally efficient quasi-optimal solutions. It is shown that Lagrangian relaxation coupled with suitable randomization cochannel multicast power control yield computationally efficient high-quality approximate solutions. For a significant fraction of problem instances, the solutions generated this way are exactly optimal. Extensive numerical results using both simulated and measured wireless channels are presented to corroborate our main findings.