Mobile Edge Computing (MEC) has been successful in proving solid support for delay-sensitive and computation-intensive applications, while invoking channel forecast enlightens a new dimension to further improve the performance. However, separately considering channel forecast and resource management fails in fully exploiting the merit of channel forecast. In this paper, we proceed in two steps. 1) By incorporating channel forecast, we extend the conventional Lyapunov optimization into multi-step-ahead Lyapunov optimization to minimize the queueing delay for non-causal scenario. 2) Based on the obtained insights, we tailor the conventional Long Short Term Memory (LSTM) into Differentiated Randomly Connected LSTM (DR-CLSTM) to obtain a desired trade-off between model complexity and forecast accuracy for the sake of delay minimization. Our simulation results highlight the performance gain of the proposed framework in terms of the system delay.

Millimeter-wave (mmWave) vehicular communication brings new technical challenges on wireless resource management due to the sensitivity to blockages and the directionality property, as conventional beam alignment techniques suffer from large communication overhead. To enable fast base station (BS) association and beam alignment, we propose a lightweight online learning framework by embracing sparse representation (SR) and Gaussian process (GP). To obtain preliminary information of the transmission environment, fingerprint-based method is advocated for static scenarios, while its performance degrades in dynamic scenarios. To incorporate the influence of vehicle motion, we innovatively propose the idea of trajectory-aware fingerprint, which further triggers the following two designs: 1) Trajectory Prediction: We utilize GP to predict the trajectory of moving vehicles. Noticing the utility of the forecast information drops fast with the computational complexity, we propose a differentiated prediction framework to balance accuracy and model complexity to maximize such utility and 2) Fingerprint Adaptation: As the existence of infinite number of trajectories, we approximate a trajectory using grayscale image, and prove the influence of such approximation on throughput is limited. Then, given a predicted trajectory, SR is invoked to perform robust fingerprint adaptation that facilitating resource management. Finally, the simulation results demonstrate the superiority of the proposed framework.

Mobile crowdsensing (MCS) is an emerging technology, which provides a promising paradigm for completing complex sensing tasks. While existing studies for MCS mainly focus on designing incentive mechanisms to attract more participants or optimizing task allocation to maximize profit, the freshness of information, known as Age of Information (AoI), has been largely overlooked. In MCS systems, some Point of Interests (PoIs) need to be monitored through sampling by participants. High-frequency sampling can effectively ensure AoI performance, which also imposes significant costs on participants. Therefore, it is necessary to allocate appropriate sampling tasks and design the corresponding sample cycles and prices for participants. In this article, we address the joint problem of incentive mechanism and task allocation. First, we adopt the contract theory to model the incentive mechanism, where the crowdsensing platform (CP) offers a set of cycle-price combinations to participants. We establish the necessary and sufficient conditions for the feasibility of the contract and subsequently derive the optimal contract structure. Second, subject to the derived contract structure, we determine the optimal task allocation under specific conditions. For more general situations, we propose an iterative algorithm, which is based on pair switching with a proven convergence guarantee. Finally, the simulation results demonstrate the efficiency of the proposed contract-based algorithm, which also outperforms other incentive mechanisms.

Fueled by the increasing of smart mobile devices, the growth of crowd sensing tasks is explosive, where the Network Service Provider (NSP) relies on the sensing users (mobile devices) to sense and transmit fresh information. Information freshness is captured by the Age of Information (AoI) metric, which is a critical factor for real-time crowd sensing tasks. Considering the individual rationality of sensing users, it is challenged to design an AoI-aware task allocation since freshness means resource consumption. In this paper, an AoI-based dynamic pricing-matching approach is proposed to motivate sensing users to participate in tasks and also complete the task allocation. The problem is modeled as a many-to-many matching with the aim of improving the social welfare. Indeed, the differentiated AoI caused by various service priorities of sensing users should take into consideration. Toward this end, we divide sensing users into sub-users and derive the closed form of their corresponding AoI. Lastly, the proposed matching algorithm is validated to achieves a stable matching with dynamic prices and ensures the individual rationality. Simulation results demonstrate the proposed matching algorithm can achieve a significantly better social welfare than existing algorithms.

Network slicing is now widely used to provide advanced services in terms of sliced resources. It is urgent for Video Streaming Service Providers (VSSPs) to guarantee the Quality of Experience (QoE) via the sliced resources from Network Service Providers (NSPs). In this paper, we propose a bandwidth-cache pricing-based network slicing approach for video streaming delivery. Specifically, the NSP slices the bandwidth and caching space jointly, and then reorganizes these resources flexibly to various VSSPs to maximize the QoE of all users. We design a bandwidth-cache pricing policy to solve the slicing problem, in which there is a trade-off between the bandwidth-cache resources in terms of QoE. We first quantitatively analyze the QoE for various resource bundles by adopting the diffusion approximation. Based on the derived QoE, we propose the heterogeneous auction, a framework for bandwidth-cache slicing with dynamic ascending prices. Specifically, the auction increases the prices of the demanded resource bundles submitted by active bidders. Later, we prove that optimal social surplus and incentive compatible can be realized. Finally, simulation results show that the proposed auction achieves better performance than conventional homogeneous auctions.

Increasing demands of multimedia contents brings a great profit to the content providers, but also a challenge of how to efficiently delivery contents to make users have a good Quality of Experience (QoE). Take the advantage of owning wireless infrastructures, the telco operator is motivated to build a content platform for entering the market of content. In this paper, we consider a content platform belonging to the telco operator, which can provide periodically-updated contents with small Age of Information (AoI), namely, fresh contents. The content update consumes radio resource resulting in a trade-off between the AoI and the latency. We adopt the contract theory to monetize contents considering the above two factors in a realistic asymmetric information scenario. Necessary and sufficient conditions are derived to ensure the feasibility of the contract. We further propose the optimal update schemes and the corresponding fees, which maximizes the utility of the operator. Simulation reveals that the proposed contract enables the users, who attach importance to the freshness, to obtain frequently updating contents.

Edge caching is an efficient technique for mitigating redundant data transmissions over backhaul links. Contents are constantly evolving, and thus the cached contents should be updated timely to guarantee freshness. Information freshness is captured by the Age of Information (AoI) metric. In this paper, we explore a content resale problem, where a Network Service Provider (NSP) purchases contents from Content Providers (CPs) then sells contents to users. We model the problem as a three-stage sequential problem. Then we decompose the problem into two sub-problems on deciding the contents to be purchased and cached, and the respective prices. We solve the sub-problems through the framework of Stackelberg game and auction respectively. For the content purchase, we consider four different cases to design the auction mechanisms. In the auctions, the NSP has to reveal its private information due to the AoI characteristics. Furthermore, we prove that such disclosure will not bring the loss of the NSP’s utility. Finally, numerical results in different cases are provided to show that the NSP can efficiently exploit the benefit from the knowledge about CPs’ production costs and the competition among CPs under our proposed mechanism.

Currently, it is still challenging for Video Streaming Service Providers (VSSPs) to develop advanced video delivery in wireless communications due to the limitation of network resources. In this paper, we propose a pricing strategy to effectively allocate heterogeneous resources, including transmission bandwidth and caching space, to improve users' Quality of Experience (QoE). Due to the properties of the two-hop video streaming model we propose, a video can be partially cached in the local storages to provide low waiting time and avoid playback interrupt. To design the pricing strategy for two heterogeneous resources, we first quantitatively analyzing the QoE with partial video caching by adopting diffusion approximation with stochastic data arrival. Based on the above QoE model, we develop an ascending auction framework for pricing heterogeneous resources. Furthermore, we prove that our proposed pricing strategy achieves asymptotically optimal social surplus and ε-incentive compatibility. Finally, simulation results show that our proposed pricing strategy can achieve better performance on the social surplus compared to the conventional pricing strategies.

Cooperative content caching has been demonstrated to achieve significant performance gain over the conventional content caching paradigm by exploiting content diversity through the participation of multiple cooperative nodes. Although cooperative content caching has the potential to increase the efficiency, an improper coalition formation may result in severe performance degradation. Therefore, the cooperative nodes should be carefully selected according to their interests in different content objects. In this paper, we develop an analytical framework for cooperative content caching from a coalitional game perspective. The cooperation issue for content caching among nodes is studied by the coalitional game theory, and the associated problems are analyzed in different cases that the utility transfer among nodes is allowed or not. If the utility transfer is allowed, by exploiting the properties of the coalitional costs, we derive the non-empty property of the core of a transferable utility coalitional game, and prove that the grand coalition is stable in spite of the presence of coalition costs. If the utility transfer is not allowed, we adopt a non-transferable utility coalitional game model. The grand coalition is not always stable in the presence of coalition costs. A merge and split algorithm is proposed to form the coalitional structure for iteratively improving the caching performance. Finally, the simulation results demonstrate the cooperation gains on both the sum and individual utilities in different scenarios.