In an era of increasing data cap constraints, optimizing video streaming quality while adhering to user-defined data caps remains a significant challenge. This paper introduces Bitrate-Adaptive Limit-Aware Netcast Content Enhancement (BALANCE), a novel Quantum framework aimed at addressing this issue. BALANCE intelligently pre-selects video segments based on visual complexity and anticipated data consumption, utilizing the Video Multimethod Assessment Fusion (VMAF) metric to enhance Quality of Experience (QoE). We compare our method against traditional bitrate ladders used in Adaptive Bitrate (ABR) streaming, demonstrating a notable improvement in QoE under equivalent data constraints. We compare the Slack variable approach with the Dynamic Penalization Approach (DPA) by framing the bitrate allocation problem through Quadratic Unconstrained Binary Optimization (QUBO) to effectively enforce data limits. Our results indicate that the DPA consistently outperforms the Slack Variable Method, delivering more valid and optimal solutions as data limits increase. This new quantum approach significantly enhances streaming satisfaction for users with limited data plans.

To address the explosive growth of number of mobile users in future generations of wireless networks, Non-Orthogonal Multiple Access (NOMA) has received significant attention. In this work, we propose an incentive driven content shifting framework to reward users which push multimedia data content from an Orthogonal Multiple Access (OMA) link to a NOMA link. The objective of the framework is to benefit both the user and the base station by simultaneously improving the user’s Quality of Experience (QoE) and the base station’s profits. The key issue of strategically splitting the data among the available NOMA and OMA links are studied to attain the best possible service. According to our study, OMA could be leveraged for transmission of important frames while NOMA is suitable for frames with lesser importance. Using game theoretic methods, we develop a method to attain the Nash Equilibrium of the solution. Simulation studies showcase the merits and potentials of the proposed incentive mechanism to encourage users transmitting certain data through NOMA links.

Non-Orthogonal Multiple Access (NOMA) is one of the promising solutions for next-generation wireless multimedia services to mitigate traffic congestion and reduce latency. Orthogonal Multiple Access (OMA) on the other hand has been used over the years to transmit content wirelessly. The merits offered by OMA would make it co-exist along with NOMA in the upcoming years. The key idea of this paper is to leverage Uber/Lyft ride-share philosophy to design the wireless access pricing model to be "orthogonality-centric"; being aware of the shared NOMA and exclusive OMA access price differences in wireless networks. The efficacy of the developed pricing model is then studied on a hybrid network which supports both NOMA and OMA resource blocks. The utility definitions for both the base station and the mobile user are mathematically modeled and the profit maximization interplay between the two parties is analyzed using game theoretic methods. An algorithm to derive Nash Equilibrium solution is presented. The simulations showcase the efficacy of the developed orthogonality driven pricing framework developed in this paper. Furthermore, the results indicate that the non-uniform orthogonality-centric pricing yields better utilities for both the base station and the end user.

Wireless multimedia communications is one of the most rapidly growing technologies of the decade. With explosive growth in network traffic, the service providers struggle to provide satisfactory Quality of Experience (QoE) to the users and increase revenue. Non-Orthogonal Multiple Access (NOMA) has been proposed to cater multiple users simultaneously in each resource block by exponentially increasing the network capacity. However, the strategic allocation and trading of NOMA resource blocks are still open issues. In this work, we treat a NOMA resource block as a commodity representing its channel quality and the importance of data it carries. We then chalkup the innovative and appealing QoE-price interplay to promote NOMA resource block trading as a suitable candidate for future-generation wireless mobile networks. The proposed framework benefits both the utilities of users and service providers. The proposed approach is solved by deriving the Nash Equilibrium of the network interactions using a best-response game. Simulations suggests further exploration of the potentials to the adaptation of proposed framework for NOMA wireless multimedia communications.

With increasing number of mobile users, leveraging economics in communications has been envisioned as a new paradigm to boost the network performance. However, the issues such as incentivizing offloading devices and improving end-user Quality of Experience (QoE) are still open challenges. The philosophy of the proposed algorithmic protocol lies in encouraging end-users to scavenge multimedia data from the neighbors (offloading users), and by doing so, maximize their QoE by taking advantage of shorter communication distance. The protocol also facilitates the wireless carriers to lower the network traffic congestion and to improve profits by setting up cache delegation communication among the offloading users and end-user for a nominal commission fee. Steadiness of the proposed game theoretic algorithmic protocol has been achieved by formulating the interaction between the service provider, offloading user and the end-user as a game. An algorithm has been developed to determine the Nash Equilibrium solution of the game which maximizes the utilities of all the parties. Simulation studies conducted using H.265 encoded multimedia data indicate a significance raise in end-user QoE and decrease in network congestion.

Non-Orthogonal Multiple Access (NOMA) is one of the promising solutions for next-generation wireless services to mitigate traffic congestion and reduce latency. However, the issues such as regulating network revenue and ameliorating end-user Quality of Experience (QoE) are still open challenges. In this work, we propose the concept of Non-Orthogonal Multiple Access Pricing (NOMAP), to bridge the gap between price and QoE in NOMA resource allocation. The framework was then applied to a NOMA network scenario, and the problems of power allocation and resource block selection were investigated. The main contribution of this paper is to show that higher utilities can be achieved by dynamically pricing the NOMA resource blocks and by allowing the users to have a choice in resource management. In addition, an algorithm to achieve optimality in terms of Nash equilibrium is provided as an implementation example. The simulations carried out using MATLAB showcase the efficacy of the developed NOMAP framework. Furthermore, the results indicate that the proposed pricing significantly outperforms the traditional uniform pricing schemes.

With the advances in wireless multimedia communication technologies and the rise in end- user experience expectations, meeting the Quality of Experience (QoE) requirements of the mobile multimedia user has become challenging for content providers and wireless carriers. In this research, we chalk up a QoE-sensitive multimedia data pricing economic model, by pricing the quality of the data rather than the quantity of the data. Utility maximization problem between the content provider (sells multimedia content), wireless carrier (provides transmission service) and the mobile user (requests multimedia data) is modeled using the proposed framework. We then formulate the aforementioned problem as a two-stage Stackelberg game and derive the Nash Equilibrium using backward induction method. Simulation study show that each player can obtain optimal strategy where the Stackelberg equilibrium exists stably. Finally, the proposed smart pricing mechanism was tested against the traditional uniform pricing scheme and the results indicate that better utilities can be achieved by leveraging the proposed scheme.

Providing user satisfaction is a major concern for on-demand multimedia service providers and Internet carriers in Wireless Communications. Traditionally, user satisfaction was measured objectively in terms of throughput and latency. Nowadays the user satisfaction is measured using subjective metrices such as Quality of Experience (QoE). Recently, Smart Media Pricing (SMP) was conceptualized to price the QoE rather than the binary data traffic in multimedia services. In this research, we have leveraged the SMP concept to chalk up a QoE-sensitive multimedia pricing framework to allot price, based on the user preference and multimedia quality achieved by the customer. We begin by defining the utility equations for the provider-carrier and the customer. Then we translate the profit maximizing interplay between the parties into a two-stage Stackelberg game. We model the user personal preference using Prelec weighting function which follows the postulates Prospect Theory (PT). An algorithm has been developed to implement the proposed pricing scheme and determine the Nash Equilibrium. Finally, the proposed smart pricing scheme was tested against the traditional pricing method and simulation results indicate a significant boost in the utility achieved by the mobile customers.