15 research outputs found
Dynamic Rate Adaptation for Improved Throughput and Delay in Wireless Network Coded Broadcast
In this paper we provide theoretical and simulation-based study of the
delivery delay performance of a number of existing throughput optimal coding
schemes and use the results to design a new dynamic rate adaptation scheme that
achieves improved overall throughput-delay performance.
Under a baseline rate control scheme, the receivers' delay performance is
examined. Based on their Markov states, the knowledge difference between the
sender and receiver, three distinct methods for packet delivery are identified:
zero state, leader state and coefficient-based delivery. We provide analyses of
each of these and show that, in many cases, zero state delivery alone presents
a tractable approximation of the expected packet delivery behaviour.
Interestingly, while coefficient-based delivery has so far been treated as a
secondary effect in the literature, we find that the choice of coefficients is
extremely important in determining the delay, and a well chosen encoding scheme
can, in fact, contribute a significant improvement to the delivery delay.
Based on our delivery delay model, we develop a dynamic rate adaptation
scheme which uses performance prediction models to determine the sender
transmission rate. Surprisingly, taking this approach leads us to the simple
conclusion that the sender should regulate its addition rate based on the total
number of undelivered packets stored at the receivers. We show that despite its
simplicity, our proposed dynamic rate adaptation scheme results in noticeably
improved throughput-delay performance over existing schemes in the literature.Comment: 14 pages, 15 figure
PRAM: Penalized Resource Allocation Method for Video Services
The human visual system response to picture quality degradation due to packet loss is very different from the responses of objective quality measures. While video quality due to packet loss may be impaired by at most for one Group of Pictures (GOP), its subjective quality degradation may last for several GOPs. This has a great impact on resource allocation strategies, which normally make decisions on instantaneous conditions of multiplexing buffer. This is because, when the perceptual impact of degraded video quality is much longer than its objective degradation period, any assigned resources to the degraded flow is wasted. This paper, through both simulations and analysis shows that, during resource allocation, if the quality of a video stream is significantly degraded, it is better to penalize this degraded flow from getting its full bandwidth share and instead assign the remaining share to other flows preventing them from undergoing quality degradation
Scalable Video Streaming for Single-Hop Wireless Networks Using a Contention-Based Access MAC Protocol
Limited bandwidth and high packet loss rate pose a serious challenge for video streaming applications over
wireless networks. Even when packet loss is not present, the bandwidth fluctuation, as a result of an arbitrary number of active flows in an IEEE 802.11 network, can significantly degrade the video quality. This paper aims to enhance the quality of video streaming applications in wireless home networks via a joint optimization of video layer-allocation technique, admission control algorithm, and medium access control (MAC) protocol. Using an Aloha-like MAC protocol, we propose a novel admission control framework, which can be viewed as an optimization problem that maximizes the average quality of admitted videos, given a specified minimum video quality for each flow. We present some hardness results for the optimization problem under various conditions and propose some heuristic algorithms for finding a good solution. In particular, we show that a simple greedy layer-allocation algorithm can perform reasonably well, although it is typically not optimal. Consequently, we present a more expensive heuristic algorithm that guarantees to approximate the optimal solution within a constant factor. Simulation results demonstrate that our
proposed framework can improve the video quality up to 26% as compared to those of the existing approaches