Variable bitrate (VBR) encoding has gained considerable interest due to its capacity to enhance video quality and mitigate transmission congestion in contrast to constant bitrate (CBR) encoding. However, adaptive bitrate (ABR) streaming faces challenges when dealing with VBR-encoded videos, primarily stemming from the significant variability in chunk size and the consequent bitrate fluctuations. This paper proposes CoarseUCB, a context-aware online learning algorithm for bitrate adaptation in VBR-encoded videos. CoarseUCB considers important aspects of VBR-encoded video streaming and uses the upper confidence bound (UCB) method for bitrate selection. The UCB method does not require precise bandwidth estimation and balances the exploration and exploitation of each action effectively. Additionally, CoarseUCB accounts for the impact of multiple future video chunks when making the bitrate decision for the current chunk. To evaluate the effectiveness of CoarseUCB, we conduct experiments to assess its efficiency. The results show that CoarseUCB delivers a higher average user quality of experience (QoE) compared to state-of-the-art ABR algorithms, resulting in an improvement of up to 9.81%.

Sketch-based image retrieval (SBIR) is a growing research field due to its potential application in both commercial value and academic value. Using sketches to retrieve images is an ideal substitute to text retrieve in capturing the fine-grained visual information. However, allowing to retrieve natural images from ambiguous and abstract sketch remains a challenging problem in computer vision and image processing. The main challenge of this task is not only the domain difference between free-hand sketch and natural image but also the abstraction of sketch. In this paper, in order to tackle this challenging task, we introduce a novel strategy named stroke harmonization, which is aim at effectively narrow the sketch-image domain gap by explicitly aligning strokes of sketches and natural images to the same stroke style domain. Based on the stroke harmonization, we propose a novel network, named StrokeNet, which consists of generative model and feature extraction model, to harmonizing free-hand sketch and natural image to the same intermediate domain, i.e., stroke domain, and then perform sketch-based image retrieval in this domain. The proposed method is comprehensively evaluated on two representative fine-grained SBIR datasets, QMUL-ShoeV2 and QMUL-ChairV2. The experiments consistently indicate that our method gains the superior accuracies over recent state-of-the-art methods without relying on complex architectures.

Variable Bitrate (VBR) video encoding can provide much high quality-to-bits ratio compared to the widely adopted Constant Bitrate (CBR) encoding, and thus receives significant attentions by content providers in recent years. However, it is challenging to design efficient adaptive bitrate algorithms for VBR-encoded videos due to the sharply fluctuating chunk size and the resulting bitrate burstiness. In this paper, we propose a neural adaptive streaming framework called Vibra for VBR-encoded videos, which can well accommodate the high fluctuation of video chunk sizes and improve the quality-of-experience (QoE) of end users significantly. Our framework takes the characteristics of VBR-encoded videos into account, and adopts the technique of deep reinforcement learning to train a model for bitrate adaptation. We also conduct extensive trace-driven experiments, and the results show that Vibra outperforms the state-of-the-art ABR algorithms with an improvement of 8.17% -- 29.21% in terms of the average QoE.

Vehicular fog computing (VFC) has become an appealing paradigm to provide services for vehicles and traffic systems. However, high mobility is one of the great challenges to the communication and computation service qualities in VFC. A network model for directional vehicle mobility is proposed in this paper to guarantee the service qualities of vehicles in VFC. In the model, vehicles are configured into three vehicular subnetworks according to their turning directions at the next crossing. For each subnetwork, vehicles communicate with each other via vehicle-to-vehicle communication, and with roadside units via vehicle-to-infrastructure communication. The aim is to minimize the average response time of the tasks originated from vehicles. By carefully choosing neighboring vehicles as task processing helpers, a greedy algorithm is proposed to solve the mentioned optimization problem. Besides, two bipartite matching based algorithms, named BMA1and BMA2, are proposed by exploiting Kuhn-Munkras approach and minimum-cost maximum-flow approach, respectively. Performance of the proposed model and the offloading algorithms are evaluated on the combined simulation platform by open street map, SUMO and NS-3. Simulation results show that, the proposed model outperforms four existing models in terms of average response time, when the five models have similar number of unsuccessful tasks. Moreover, the proposed BMA1and BMA2are superior to the existing greedy algorithm in terms of the average response time of tasks, and the proposed greedy algorithm significantly accelerates the generation of offloading decisions in comparison to BMA1, BMA2and the existing greedy algorithm.

In heterogeneous cellular network, task scheduling for computation offloading is one of the biggest challenges. Most works focus on alleviating heavy burden of macro base stations by moving the computation tasks on macro cell user equipment (MUE) to remote cloud or small cell base stations. But the selfishness of network users is seldom considered. Motivated by the multiple access mobile edge computing, this paper provides incentive for task transfer from macro cell users to small cell base stations. The proposed incentive scheme utilizes small cell user equipments to provide relay services. The problem of computation offloading is modeled as a two-stage auction, in which the remote MUEs with common social character can form a group and then buy the computation resource of small cell base stations with the relaying of small cell user equipment. A two-stage auction scheme named TARCO is contributed to maximize utilities for both sellers and buyers in the network. The truthfulness, individual rational and budget balance properties of TARCO are also proved in this paper. In addition, two algorithms are proposed to further refine TARCO on the social welfare of the network. One can achieve higher utility of MUEs and the other can obtain higher total social welfare. Extensive simulation results demonstrate that, TARCO is better than random algorithm by 104.90 percent in terms of average utility of MUEs, while the performance of TARCO is further improved up to 28.75 percent and 17.06 percent by the proposed two algorithms, respectively.