Abstract—We consider a situation where a video sequence is to be compressed and transmitted over a wireless channel. Our goal is to limit the amount of distortion in the received video sequence, while minimizing transmission energy. To accomplish this goal, we consider error resilience and concealment techniques at the source coding level, and transmission power management at the physical layer. We jointly consider these approaches in a novel framework. In this setting, we formulate and solve an optimization problem that corresponds to minimizing the energy required to transmit video under distortion and delay constraints. Experimental results show that simultaneously adjusting the source coding and transmission power is more energy efficient than considering these factors separately. Index Terms—Error concealment, error resilience, expected distortion, optimal mode selection, power and rate control. I.

Abstract — In this paper, we study minimal power transmission of bursty sources over wireless channels with constraints on mean queuing delay. The power minimizing schedulers adapt power and rate of transmission based on the queue and channel state. We show that packet scheduling based on queue state can be used to trade queuing delay with transmission power, even on additive white Gaussian noise channels. Our extensive simulations show that small increases in average delay can lead to substantial savings in transmission power, thereby providing another avenue for mobile devices to save on battery power. We propose a lowcomplexity scheduler that has near optimal performance. We also construct a variable rate QAM based transmission scheme to show the benefits of the proposed formulation in a practical communication system. Power optimal schedulers with absolute packet delay constraints are also studied and their performance is evaluated via simulations. Index Terms — Packet scheduling, power control, queuing delay, traffic regulation, wireless channels. I.

In this paper we discuss a selection of promising and interesting research areas in the design of protocols and systemsforwirelessindustrialcommunications.Wehaveselected topicsthathaveeitheremergedashottopicsintheindustrial communicationscommunityinthelastfewyears(likewireless sensornetworks),orwhichcouldbeworthwhileresearchtopicsin thenextfewyears(forexamplecooperativediversitytechniques for error control, cognitive radio/opportunistic spectrum access for mitigation of external interferences).

In this paper, packet scheduling with maximum delay constraints is considered with the objective to minimize average transmit power over Gaussian channels. The main emphasis is on deriving robust schedulers which do not rely on the knowledge of the source arrival process. Towards that end, we first show that all schedulers (robust or otherwise) which guarantee a maximum queuing delay for each packet are equivalent to a time-varying linear filter. Using the connection between filtering and scheduling, we study the design of optimal power minimizing robust schedulers. Two cases, motivated by filtering connection, are studied in detail. First, a time-invariant robust scheduler is presented and its performance is completely characterized. Second, we present the optimal time-varying robust scheduler, and show that it has a very intuitive time water-filling structure. We also present upper and lower bounds on the performance of power-minimizing schedulers as a function of delay constraints. The new results form an important step towards understanding of the packet time-scale interactions between physical layer metric of power and network layer metric of delay.

We seek to maximize the data throughput of an energy and time constrained transmitter sending data over a fading channel. The transmitter has a fixed amount of energy and a limited amount of time to send data. Given that the channel fade state determines the throughput obtained per unit of energy expended, the goal is to obtain a policy for scheduling transmissions that maximizes the expected data throughput. We develop a dynamic programming formulation that leads to an optimal closed-form transmission schedule. We then extend our approach to the problem of minimizing the energy required to send a fixed amount of data over a fading channel given deadline constraints.

Abstract—Rapid growth in wireless networks is fueling demand for video services from mobile users. While the problem of transmitting video over unreliable channels has received some attention, the wireless network environment poses challenges such as transmission power management that have received little attention previously in connection with video. Transmission power management affects battery life in mobile devices, interference to other users, and network capacity. We consider energy efficient transmission of a video sequence under delay and quality constraints. The selection of source coding parameters is considered jointly with transmitter power and rate adaptation, and packet transmission scheduling. The goal is to transmit a video frame using the minimal required transmission energy under delay and quality constraints. Experimental results are presented that illustrate the advantages of the proposed approach. Index Terms—Energy efficiency, power and rate control, video streaming, wireless video. I.

A game-theoretic model is proposed to study the cross-layer problem of joint power and rate control with quality of service (QoS) constraints in multiple-access networks. In the proposed game, each user seeks to choose its transmit power and rate in a distributed and selfish manner in order to maximize its own utility and at the same time satisfy its QoS requirements. The user’s QoS constraints are specified in terms of the average source rate and an upper bound on the average delay where the delay includes both transmission and queueing delays.. The utility function considered here measures the number of reliable bits transmitted per Joule of energy consumed and is particularly suitable for wireless networks in which energy efficiency is important. The Nash equilibrium solution for the proposed non-cooperative game is derived and a closed-form expression for the utility achieved at equilibrium is obtained. It is shown that the QoS requirements of a user translate into a “size ” for the user which is an indication of the amount of network resources consumed by the user. Using this framework, the tradeoffs among throughput, delay, network capacity and energy efficiency are also studied. In addition, we give analytical expressions for users ’ delay profiles and quantify the delay performance of the users at Nash equilibrium.

In wireless fading channels, multiuser diversity can be exploited by scheduling users so that they transmit when their channel conditions are favorable. This leads to a sum throughput that increases with the number of users and, in certain cases, achieves capacity. However, such scheduling requires global knowledge of every user’s channel gain, which may be difficult to obtain in some situations. This paper addresses contention-based protocols for exploiting multiuser diversity with only local channel knowledge. A variation of the classic ALOHA protocol is given in which users attempt to exploit multi-user diversity gains, but suffer contention losses due to the distributed channel knowledge. We characterize the growth rate of the sum throughput for this protocol in a backlogged system under both short-term and long-term average power constraints. A simple “fixed-rate ” system is shown to be asymptotically optimal and to achieve the same growth rate as in a system with a centralized scheduler. Moreover, asymptotically, the fraction of throughput lost due to contention is shown to be 1/e. Also, in a system with random arrivals and an infinite user population, a variation of this ALOHA protocol is shown to be stable for any total arrival rate, given that users can estimate the backlog. I.

In this paper, we present a simplified scheduling scheme for packet transmission over a fading channel which is modeled as a finite state block channel. We first derive the optimal minimum power transmission policy with constraints on both average delay and packet loss. This problem is seen to be the dual problem of the work by Rajan et. al. [1] where the packet loss rate is minimized under constraints on average delay and power. The optimal policy requires a sophisticated tablelook -up for implementation. In order to alleviate this problem, we design a simplified transmission policy that is based on checking for three control parameters: a transmission rate threshold, a channel state threshold and the transmission buffer size. Our results show that the minimum average power with the simplified scheme is very close to that achieved by the optimal policy. By relaxing the packet loss constraint, the simplified policy is also found to allow reduced buffer sizes, thereby simplifying system implementation. With the simplified scheduling policy, the transmitter can be modeled as a bulk service queue and an upper bound for the average delay is derived. Further, the packet loss rate and the average transmit power are estimated using an imbedded Markov chain technique.

We consider a slotted system with N queues, and i.i.d. Bernoulli arrivals at each queue during each slot. Each queue is associated with a channel that changes between "on" and "off" states according to i.i.d. Bernoulli processes. We assume that the system has K identical transmitters ("servers"). Each server, during each slot, can transmit up to C packets from each queue associated with an "on" channel. We show that a policy that assigns the servers to the longest queues whose channel is "on" minimizes the total queue size, as well as a broad class of other performance criteria. We provide several extensions, as well as some qualitative results for the limiting case where N is very large. Finally, we consider a "fluid" model under which fractional packets can be served, and subject to a constraint that at most C packets can be served in total from all of the N queues. We show that when K = N , there is an optimal policy which serves the queues so that the resulting vector of queue lengths is "Most Balanced."