We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer.The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.

We consider the problem of optimizing video delivery for a network supporting video clients streaming stored video. Specifically, we consider the problem of jointly optimizing network resource allocation and video quality adaptation. Our objective is to fairly maximize video clients' Quality of Experience (QoE) realizing tradeoffs among the mean quality, temporal variability in quality, and fairness, incorporating user preferences on rebuffering and cost of video delivery. We present a simple asymptotically optimal online algorithm, NOVA, to solve the problem. NOVA is asynchronous, and using minimal communication, distributes the tasks of resource allocation to network controller, and quality adaptation to respective video clients. Video quality adaptation in NOVA is also optimal for standalone video clients, and is well suited for use with DASH framework. Further, we extend NOVA for use with more general QoE models, networks shared with other traffic loads and networks using fixed/legacy resource allocation. arXiv:1307.7210v3 [cs.NI] 25 Sep 2013 4 models, [14] and [15] study the resource allocation component for video delivery accounting for user dynamics. A major weakness of the aforementioned papers is the limited nature of the associated QoE models (that are essentially just the mean quality) and their lack of flexibility in managing/incorporating user preferences related to rebuffering and cost.While [8] presents a novel algorithm for realizing mean-variability tradeoffs for video delivery (see [18] for genearalizations), the model involves a strong assumption of synchrony-the download of a segment of each video client starts at the beginning of a (network) slot and finishes by the end of the slot. This assumption on synchrony precludes any explicit control over rebuffering as it limits the ability of a video client to get ahead (by downloading more segments) during periods when channel is good and/or network is underloaded. Relaxed/different versions of this assumption can be found in the theoretical frameworks used in many previous papers (e.g., decision making in [16], [12], [13] is synchronous) as it facilitates an easier extension of tools from classical NUM framework. However, this assumption of synchrony is not ideal for DASH-based video clients in a wireless network that operate 'at their own pace'-downloading variable sized segments (with variable download times) one after the other. In this paper, we drop the assumption of synchrony which allows us to exploit opportunism across video clients' state of playback buffer (channels and features of video content like quality rate tradeoffs), and base our adaptation decision concerning a segment on network state information relevant to the download period of the segment. We also tackle the consequent novel technical challenges related to distributed asynchronous algorithms operating in a stochastic setting. Further, the rebuffering constraint in our asynchronous setting effectively induces a new type of constraint involving averages measured over two time...

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Abstract-We study sensor networks with energy harvesting nodes. The generated energy at a node can be stored in a buffer. A sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time at the node. For such networks we develop efficient energy management policies. First, for a single node, we obtain policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable suboptimal policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay. Next using the results for a single node, we develop efficient MAC policies.