Pricing model for internet-based multi-class Video-On-Demand(VOD) services

Abstract

This paper presents an optimal Pay-Per-View (PPV) price decision model for maximizing an Internet based Video-On-Demand (VOD) service provider's revenues, taking into account the service provider's service quality and consumers' willingness to pay. The model considers multi-class VOD services with differentiated qualities and determines the optimal price for each class through simulations. The simulation results show that as long as all the multi-class services have non-zero demands, the differential pricing system provides more revenues than the uniform pricing system that prevails in the current VOD market. To test the robustness of the model, simulations were performed with gradually increasing customer demands or system workloads. The simulation results show that even with substantial customer demands or system workloads, self-adjustment mechanism of the model works and the system reaches a stable status in equilibrium. This paper also presents a numerical example of guaranteeing Quality-of-Service (QoS) through pricing strategies as a short-term measure.

Consumer benefit versus price elasticity of demand: a nonlinear complex system model of pricing internet services on QoS-centric architecture

“…(g) In the context of digital economics supported by Internet services, the underlying sophistication in Internet pricing is addressed in [19] as a dynamic adjustment of charges being elucidated via a reinforcement learning model. (h) Described in [12], are multi-class services (such as VoD) supported by the Internet and in making of a consistent price-decision model thereof, the algorithm developed maximizes the service provider's revenues constrained by QoS and consumers' WP; and (i) the game-theoretic approach is revisited in [30] for deducing a practical pricing model of inter-domain, IP-based multicasting. Additional game-theoretical considerations of, for instance, user loyalty and bundle pricing can be found in [1,13].…”

“…12 ← Obtain output set on V/V versus E for an ensemble run of 100 (and the ensemble average) by computing the function f and plot the simulation results…”

Optimal Pricing Strategies for Capacity Leasing Based on Time and Volume Usage in Telecommunication Networks

“…Some researches argue that the greatest advantage of volume‐based pricing for operators is that the uncertainty and risk of consumption remains with the customer, rather than the operator, which can prorate bills based on the data volumes transferred (Biggs & Kelly, ). The pay‐per‐volume context is evaluated in many respects in recent research studies (Altmann, Rupp, & Varaiya, ; Bouras & Sevasti, ; Anderson, Kelly, & Steinberg, ; Kim, ; Levy et al, ).…”