Public bus services are often planned in the context of urban planning. For a city with efficient and extensive network of public transportation system like Singapore, enhancing the existing coverage of bus service to meet the dynamic mobility needs of the population requires data mining approach. Specifically, frequent taxi rides between two locations at a period of time may suggest possible poor coverage of public transport service, if not lacking of the public transport service. In this paper, we describe a proof of concept effort to discover this weakness and its improvement in public transportation system via mining of taxi ride dataset. We cluster taxi rides dataset to determine some popular taxi rides in Singapore. From the clustered taxi rides, we filter and select only the clusters whose commuting via existing public transport are tortuous if not unreachable door-to-door. Based on the discovered travel pattern, we propose new bus routes that serve the passengers of these clusters. We formulate the bus planning problem as an optimization of directed cycle graph, and present it's preliminary solution and results. We showcase our idea in the case of Singapore.

In a mobile cloud gaming, high-quality, high-frame-rate game images of immense data size need to be delivered to the clients over wireless networks under stringent delay requirement. For good gaming experience, reducing the transmission bit rate of the game images is necessary. Most existing cloud gaming platforms simply employ standard, off-the-shelf video codecs for game image compression. In this paper, we propose the layered coding scheme to reduce transmission bandwidth and latency. We leverage the rendering computation of modern mobile devices to render a low-quality local game image, or the base layer (BL). Instead of sending a high-quality game image, cloud servers can send enhancement layer information, which clients can utilize to improve the quality of the BL. Central to the layered coding scheme is the design of a complexity-scalable BL rendering pipeline that can be executed on a range of power-constrained mobile devices. In this paper, we focus on the lighting stage in modern graphics rendering and propose a method to scale the popular Blinn-Phong lighting for the use in BL rendering. We derive an information-theoretic model on the Blinn-Phong lighting to estimate the rendered image entropy. The analytic model informs the optimal BL rendering design that can lead to maximum bandwidth saving subject to the constraint on the computation capability of the client. We show that the information rate of the enhancement layer could be much less than that of the high-quality game image, while the BL can be generated with only a very small amount of computation. Experiment results suggest that our analytic model is accurate in estimating. For layered coding scheme, up to 84% reduction in bandwidth usage can be achieved by sending the enhancement layer information instead of the original high-quality game images compressed by H.264/AVC.

In mobile cloud gaming, images are rendered, compressed, and delivered to mobile devices from cloud servers. Joint optimization of rendering and coding aims to reduce data rate of the delivery. Among the tasks in a rendering pipeline, Blinn-Phong lighting is the most widely adopted lighting model and also has the most impact on the visual information of the rendered images. In this paper, we analyze and model the information content of an image rendered using the Blinn-Phong lighting model. Our analytic model estimates the entropy generated by the Blinn-Phong reflections on the rendered images. In the context of cloud gaming, we derive the solution for the joint rendering and coding optimization, and demonstrate how the entropy estimators can facilitate fast and on-the-fly solution to the optimization. Experiments results validate our information-theoretic analyses, and show substantial bitrate reduction by the joint optimization of rendering and coding.

Wireless video chat is a power-consuming and bitrate-intensive application. Unlike video streaming, which is one-way traffic, video chat features distributed two-way traffic relayed via base stations, where resource allocation of a client affects the video quality seen by its communicating partner. In this paper, we study the mechanism design of this application via dynamic pricing, and seek efficiency and fairness of resource utilization. Specifically, we assume that the base station relays video bitstreams and charges a service price on the clients based on the transmission power consumption. Based on the price and a given power budget, the clients allocate bitrate and power for video coding and transmission such that the service price and the distortion seen by their partners are minimized. We study such network dynamics in Stackelberg game-theoretic framework. To solve the problem, we propose a complexity-scalable video encoding method and a power-rate-distortion (PRD) model for video chat. The model is more accurate in describing the PRD characteristics, yet of lower complexity in online updates of its coefficients. Based on the PRD model, we derive the distributed rate and power allocations for the clients. We show that a simple pricing update in the base stations is sufficient for optimal pricing. The proposed algorithms are optimal and converge to the Stackelberg equilibrium. Existing SNR- and power-based pricing schemes could not ensure fairness and efficiency simultaneously. We propose a hybrid pricing scheme that balances these conflicting criteria. Extensive simulations demonstrate superior performance of the proposed methods and solutions.

In mobile cloud gaming, one of the main challenges is to deliver high-quality game images over wireless networks under stringent delay requirement. To reduce the bit-rate of game images, we propose Layered Coding, which leverages the graphic rendering capability of modern mobile devices to reduce transmission bit-rate. Specifically, we render a low-quality local game image, or the base layer, on the power-constrained mobile client. Instead of sending the high quality game image, the cloud server sends enhancement layer information, which the client utilizes to improve the quality of the base layer. Central to the proposed layered coding is the design of base layer (BL) rendering. We discuss BL design and propose a computationally-scalable Phong lighting that can be used in BL rendering. We performed experiments to compare our layered coding with state-of-the-art, which uses H.264/AVC inter-frame coding to compress game images. With game sequences of different model complexity and motion, our results suggest that layered coding requires substantially lower data-rate. We made available game video test sequences to stimulate future research.

Video chat is a power and rate-intensive application which requires efficient resource utilization. Unlike video streaming which is generally one way, video chats characterize distributed two way traffics relayed via base stations. In this paper, we propose a distributed rate and power allocation framework for joint coding and transmission in wireless video chats. The base station imposes a service charge, which considers relay transmission power as a cost, for relaying video bitstreams. For clients, we derive the optimal rate and power allocation for video coding and transmission such that the network service charge and video distortion are minimized under a power constraint. For the base station, existing pricing schemes could not ensure fairness and efficiency simultaneously. We propose an optimal hybrid pricing scheme which allows balanced tradeoff between fairness and efficiency in network service. Network dynamics of video chats can be analyzed in the Stackelberg game framework, and shown to converge to the Stackelberg equilibrium. Extensive simulations confirm the performance analysis of the proposed solutions and the network dynamics.

The Scalable Video Coding (SVC) standard offers multiple scalabilities while maintaining high coding efficiency. However, the joint coding of multiple scalabilities complicates the rate adaptation as the SVC packets possess different priorities. To address this challenge, we propose in this paper a fast rate adaptation scheme for SVC in absence of the original video sequence. Specifically, we present an efficient algorithm to extract the SVC bitstream by a packet prioritization scheme, which is based on the analysis of packet dependencies (PD) in encoding of full scalability. Experimental results demonstrate that the proposed scheme achieves significant improvement in PSNR over the basic bit extraction scheme in SVC and attains comparable PSNR performance to the Quality Layer based rate adaptation scheme while significantly reducing the computational cost.

Wireless video chat is a power-consuming and of high bitrate application. To prolong the operational lifetime, optimal rate and power allocations in joint coding and transmission are necessary. We exploit low motion and high inter-frame correlation in video chats to determine a complexity-scalable video coding adaptation which is Pareto optimal. We propose a model that describes power-rate-distortion (PRD) characteristic of the complexity scalable video coding more accurately. As video contents are non-stationary, we formulate an online algorithm for the model's parameters updates. We demonstrate that the PRD model with online updates can be applied to solve the rate and power allocation problem optimally in joint coding and transmission. Simulation results confirm that the model describes PRD characteristics more accurately via online recursive updates. The resource allocation scheme which is based on the PRD model yields better video quality than recent methods in a resource-constrained wireless video chat application.

In wireless video transmissions, encoded video frames are often large in data load and truncated into many transport packets (TPs) for reliable transmissions. These TPs are to be delivered before a deadline at certain reliability that depends on the importance of the video frame. High power and bitrate transmission schemes are often deployed to ensure low loss rate, but at the cost of substantial energy consumption. This paper addresses the energy-minimizing transmission policy for highly reliable transmissions of a group of TP with a common deadline. We jointly adapt the transmission rate, transmission power, and retransmission limit to minimize the transmission energy while ensuring that the video frame is reliably delivered before a deadline. In a slow fading channel, we formulate a deterministic transmission policy that allocates a retransmission limit to the TPs and jointly optimizes with the transmission bitrate and power. Contrary to the intuition that avoids packet loss and retransmission to preserve energy, we demonstrate that allowing some retransmissions in the joint optimization consumes less energy, without compromising the target reliability. In a Rayleigh fading channel, a conventional approach adopts a supportable transmission rate and declares off-channel at deep fading. We generalize the approach to include various transmission schemes at different fading states and propose the probabilistic combination of these schemes. Given the fading statistics and a channel state, our policy determines to pause or to deploy a proper transmission scheme such that the video frame transmission is energy minimized, timely, and highly reliable. Extensive simulations confirm that the proposed transmission policies consume less energy than existing methods. In particular, when the deadline or the target reliability are tightened, the proposed policies yield even higher energy efficiency.

In wireless video streaming, a video frame is usually large in data load and is truncated into multiple transport packets for reliable transmissions. These transport packets should be transmitted successfully before a deadline for frame decoding. High bitrate and high power transmission schemes are often deployed to ensure reliable transmissions. Such a method however incurs substantial energy consumption. Unlike existing methods which assign the retransmission limit independently to each transport packet, we share the retransmission opportunity among transport packets of a video frame, and jointly optimize it with transmission rate and power such that the video frame delivery is energy efficient. Contrary to the common intuition that avoids packet loss and retransmission to preserve energy, our approach yields lower energy consumption by judiciously inducing loss and retransmission of the transport packets, without violating the deadline and reliability constraint. Numerical results reveal that the joint optimization improves energy efficiency in wireless video transmissions. The improvement is even more significant when the allocated transmission time is limited or the reliability constraint is tightened.

In this paper, we investigate optimal resource allocation and scheduling for scalable video multicast over wireless networks. The wireless video multicasting is a best-effort service which has limited transmission energy and channel access time. To cater for multi-resolution videos to heterogeneous clients and for channel adaptation, we adopt scalable video coding (SVC) with spatial, temporal and quality scalabilities. Our scalable video multicast system consists of a channel probing stage to gather the channel state information and a transmission stage to multicast videos to clients. We formulate the optimal resource allocation problem by maximizing the video quality of the clients subject to transmission energy and channel access constraints. We show that the problem is a joint optimization of the selection of modulation and coding scheme (MCS), and the transmission power allocation. By imposing a quality-of-service (QoS) constraint on the packet loss rate, we simplify the original problem to a binary knapsack problem which can be solved by a dynamic programming approach. Specifically, we first propose a multicast scheduling scheme based on the quality impact of each SVC layer. Guided by the content-aware multicast scheduling, we optimize the resource allocation for each SVC layer sequentially. Solution at each step takes into account of the channel condition, remaining resources, and client requirements. The proposed scheme is of linear complexity and leads to the maximized video quality for the admitted clients, while satisfying the energy budget and channel access constraints. Experiment results demonstrate that our scheme achieves notable video quality improvements for multicast clients, when compared to the state-of-the-art video multicast method.

Contention-based wireless networks make use of random access delay in the medium access control (MAC) layer to share the channel efficiently among users. This channel access scheme poses challenges to video traffic which is delay-sensitive. When channel access is limited, how to optimally allocate channel access and judiciously drop video packets remains an open issue. In this paper, we investigate the optimal channel access allocation and preemptive packet drop strategy for scalable video packets using contention-based MAC. We first establish a relationship to associate the characteristics of video packets, the cumulative distribution function of channel access delays and the expected video quality at the receiver. Based on the relationship, we define a video utility function that explicitly takes into account cumulative distribution of channel access delay over contention-based wireless links, video packet length and truncation, packet loss rate and quality impacts of SVC layers. As the analytical model of the video utility function is rather complex for optimization, we further propose an approximation model for the video utility function to transform the optimization problem into a convex problem which is a solvable by using the dual decomposition method. Simulation results confirm that our proposed method achieves better PSNR performance over the existing approach, especially when the total channel access for the video traffic is limited.

We present an efficient resource allocation algorithm for scalable video multicast over wireless links with limited channel access time and transmit energy budget. We first introduce a multicast architecture for the scalable extension of H.264/AVC based video delivery over IEEE 802.11 WLANs. We show that the resource allocation problem is a mixed-integer program for which we propose an efficient solution. Our algorithm explicitly takes into account of relevant factors, such as quality demands of multicast clients, wireless channel conditions, packet length, and different priorities of SVC layers. We maximize the utility of video streaming whilst satisfying channel access and energy constraints. The proposed solution jointly considers PHY mode selection and SNR allocation. Our simulation results demonstrate that the proposed algorithm achieves significant performance improvements for multicast clients in resource-constrained wireless networks.

Scalable Video Coding (SVC) provides an efficient solution for video adaptation to satisfy different requirements from heterogeneous mobile clients due to their display sizes and channel conditions. Based on the encoding structure and dependency relationship, SVC packets are entitled with different priorities in presenting quality of video sequences. In this paper, a functional model is derived to calculate packet priority index for multiple scalable video streaming to heterogeneous mobile clients. SVC packet layer ID information is utilized for packet prioritization which provides a fast and efficient implementation in packet scheduling. The accuracy and efficacy of the model are validated by comparisons with the maximum performance achieved from the distortion based prioritization. The generality is considered under different coding structures and the utility is convinced through comparisons with conventional streaming schemes in which different resolution videos are transmitted through IEEE 802.11e wireless networks.

We present a novel method of cross-layer adaptation for scalable video coding (SVC) extension of H.264/AVC multicast in wireless network. Transmission strategy for the single-hop multicast adapts according to wireless channel condition and SVC layer. Efficient transmit power and unequal FEC bit budget allocations are proposed while existing adaptive technique for throughput optimization is applied. We formulated a convex optimization problem to obtain the optimal time-division scheduler for all multicasts such that the wireless network delivers all SVC layers in noisy wireless channels and severe network capacity constraints. The cross-layer adaptation technique is verified by simulation results.